多关节机器人的智能滑模变结构控制方法研究
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摘要
对于多关节机器人的轨迹跟踪控制,自适应性和鲁棒性是其控制器应具备的两种基本控制特性。滑模变结构控制因为具有较强的鲁棒性,而成为一种有效的控制方法。但由于滑模变结构控制存在抖振,在一定程度上限制了其应用和发展。为了消除滑模变结构控制这个缺点,本文研究了滑模变结构控制与智能控制相结合的控制方法。主要包括滑模变结构控制与模糊控制、神经网络、遗传算法相结合的控制方法,并取得了如下成果:
(1)研究了多关节机器人的控制结构,设计了一种并行控制结构。该控制结构具有两个优点:一是充分利用机器人已知的知识,作为滑模控制的等效控制,缩短了智能控制部分对不确定性干扰学习的时间;二是充分利用了嵌入式系统和计算机通信等最新技术的发展成果,将对机器人不确定性干扰的学习任务分配给多个微处理器,利用计算机通信进行相互协作。这样简化了学习和控制算法,提高了控制速度。该控制结构兼有集中控制和分散控制的优点,不会因为某一关节的传感器损坏而影响其它关节的工作。所以,便于故障检测和排除,增强了控制系统的可靠性,并且便于并行处理,具有很强的鲁棒性。
(2)研究了模糊控制的设计方法和万能逼近特性。提出了一种快速自适应模糊滑模控制方法,并将此方法应用到两个机器人仿真系统中:针对具有建模误差和不确定干扰的机器人,设计了一个基于快速直接自适应模糊滑模控制的仿真系统;针对参数未知的不确定机器人,设计了一个基于快速间接自适应模糊滑模控制的仿真系统。这两个系统都不再需要对机器人的未知参数进行预先估计,通过模糊系统对机器人未知参数的逼近,使控制器的参数能随着机器人参数的变化而自适应地变化。因此,消除了滑模变结构控制的抖振。另外,文中利用李亚普诺夫稳定性定理证明了系统的稳定性,并详细分析了这两种控制系统的误差运行轨迹,从理论上证明了控制系统的误差只与模糊系统的逼近误差有关,而与系统的建模误差和干扰无关,所以该方法提高了控制系统精度、增强了控制系统的鲁棒性。
(3)研究了神经网络的设计和学习方法。针对具有建模误差和不确定干扰的机器人,提出了一种基于径向基函数的快速神经滑模控制方法。该方法通过神经网络在线学习机器人不确定性干扰的上界,消除了滑模控制的抖振。并利用李亚普诺夫稳定性定理推导出了神经网络的目标函数,保证了系统的稳定性。另外,文中详细分析了控制系统的误差特性和鲁棒性。
(4)研究了同时具有模糊控制和神经网络优点的模糊神经网络。针对具有建模误差和不确定干扰的机器人,提出了一种基于自组织模糊神经网络的全程滑模控制方法。该方法将模糊神经网络的结构学习和参数学习结合在一起,根据控制系统的性能要求自组织模糊规则,并利用梯度下降算法在线调整模糊神经网络的参数和权值,从而提高了控制系统的精度,消除了滑模控制的抖振。
(5)研究了遗传算法的设计方法。针对参数未知的不确定机器人,提出了一种基于改进遗传算法的模糊神经滑模控制方法。该方法首先利用神经网络对机器人进行数学建模,然后利用遗传算法离线优化模糊神经网络的参数,提高了控制系统的在线学习速度,最后利用梯度算法在线调节模糊神经网络的权值,使控制器参数能够随机器人参数的变化而变化,削弱了滑模控制的抖振。另外,为了解决简单遗传算法的不成熟收敛和收敛速度慢的问题,对实数编码的遗传算法设计了一种自适应遗传变异算法。该算法能有效实现全局优化,提高进化效率,对求解复杂的优化问题具有广泛的适应性。
Adaptability and robustness are the basic control characteristic for the trajectory tracking control of multi-link robots. Sliding mode variable structure control (SMVSC) is an efficient approach because of its strong robustness against disturbances and variation of parameters. However, its application and development are limited by the chattering of the SMVSC systems. In order to alleviate chattering, merging SMVSC with intelligent control are efficient approaches, such as fuzzy sliding mode control, neural sliding mode control, fuzzy neural sliding mode control based on genetic algorithm, and so on. These approaches are studied and developed in this paper:
1. Various control structures for multi-link robots are studied and a parallel control structure is presented in this paper. The parallel control structure has two merits: (1) It can take full advantage of the known information about robots, which gives the equivalent control and reduces the learning time of intelligent control for the uncertainty. (2) The advanced techniques, such as embedded systems and computer communication, are applied to increase the response speed of the control systems. The control requirement can be distributed to several microprocessors that are connected each other by control networks. Therefore, every microprocessor has only simple task, fast learning algorithm and control speed. Also, the network control structure has virtues of both centralized control structure and distributed control structure. If one of the control subsystems is damaged, the other control subsystems can continue to work and the reliability of whole control system is developed. This virtue of distributed systems is convenient to detect and remove system trouble. The parallel working ability can develop robustness of the whole system.
2. Designing approaches and general approximate characteristic of fuzzy systems are studied. Two novel control approaches are advanced: one is a fast direct adaptive fuzzy sliding mode control for multi-link robots with model errors; another is a fast indirect adaptive fuzzy sliding mode control for multi-link robots with uncertainty. The fuzzy systems are applied to approximate the unknown parameters instead of estimating them in advance. Therefore, the controller can adaptively adjust its parameters with respect to parameter changes of the controlled system. The chattering of sliding mode control is alleviated without sacrificing the system robustness. The stability of the controller is proved by using Lyapunov direct method. The errors trajectory of the control systems is analyzed in detail. It is proved that the system errors are only relative with the approaching errors of fuzzy systems and independent of the disturbances. So the control systems have small stable errors and strong robustness.
3. Designing and learning approaches of neural networks are studied. A novel global neural sliding mode control based on radial basic function is proposed for multi-link robots with model errors and uncertain disturbances. The neural networks can learn the upper limit of model errors and uncertain disturbances. So the chattering of sliding mode control is reduced. The cost function of neural networks is deduced according to Lyapunov stable theory, which can ensure that the control systems are stable.
4. The fuzzy neural networks which have both merits of fuzzy control and neural networks control are studied. A sliding mode controller based on self organizing fuzzy neural networks is designed. The controller combines the structure learning and parameters learning. The fuzzy rules are selected by competing method according to the system required accuracy. The controller parameters are adjusted by gradient descent algorithm. So the chattering of sliding mode control is eliminated. The cost function of neural networks is deduced according to Lyapunov stable theory, which ensures that the control systems are stable.
5. Genetic algorithm is studied and applied to a fuzzy neural sliding mode controller for multi-link robots without known information. The designed steps of the control system are presented. At first, the controlled system mathematic model must be identified by a neural network. Then, genetic algorithm is applied to train the fuzzy neural networks out systems according to the system mathematic model. At last, the fuzzy neural networks are applied to real time systems which adaptively adjust parameters in systems by gradient descent algorithm with respect to the controlled system parameters. So the chattering of sliding mode control is eliminated without sacrificing its robustness. In order to avoid the slow convergence and the immature convergence of genetic algorithm, an adaptive genetic algorithm is proposed, which can realize to optimize the whole parameters range, develop the optimizing efficiency, and be suitable to complex control systems.
(1)研究了多关节机器人的控制结构,设计了一种并行控制结构。该控制结构具有两个优点:一是充分利用机器人已知的知识,作为滑模控制的等效控制,缩短了智能控制部分对不确定性干扰学习的时间;二是充分利用了嵌入式系统和计算机通信等最新技术的发展成果,将对机器人不确定性干扰的学习任务分配给多个微处理器,利用计算机通信进行相互协作。这样简化了学习和控制算法,提高了控制速度。该控制结构兼有集中控制和分散控制的优点,不会因为某一关节的传感器损坏而影响其它关节的工作。所以,便于故障检测和排除,增强了控制系统的可靠性,并且便于并行处理,具有很强的鲁棒性。
(2)研究了模糊控制的设计方法和万能逼近特性。提出了一种快速自适应模糊滑模控制方法,并将此方法应用到两个机器人仿真系统中:针对具有建模误差和不确定干扰的机器人,设计了一个基于快速直接自适应模糊滑模控制的仿真系统;针对参数未知的不确定机器人,设计了一个基于快速间接自适应模糊滑模控制的仿真系统。这两个系统都不再需要对机器人的未知参数进行预先估计,通过模糊系统对机器人未知参数的逼近,使控制器的参数能随着机器人参数的变化而自适应地变化。因此,消除了滑模变结构控制的抖振。另外,文中利用李亚普诺夫稳定性定理证明了系统的稳定性,并详细分析了这两种控制系统的误差运行轨迹,从理论上证明了控制系统的误差只与模糊系统的逼近误差有关,而与系统的建模误差和干扰无关,所以该方法提高了控制系统精度、增强了控制系统的鲁棒性。
(3)研究了神经网络的设计和学习方法。针对具有建模误差和不确定干扰的机器人,提出了一种基于径向基函数的快速神经滑模控制方法。该方法通过神经网络在线学习机器人不确定性干扰的上界,消除了滑模控制的抖振。并利用李亚普诺夫稳定性定理推导出了神经网络的目标函数,保证了系统的稳定性。另外,文中详细分析了控制系统的误差特性和鲁棒性。
(4)研究了同时具有模糊控制和神经网络优点的模糊神经网络。针对具有建模误差和不确定干扰的机器人,提出了一种基于自组织模糊神经网络的全程滑模控制方法。该方法将模糊神经网络的结构学习和参数学习结合在一起,根据控制系统的性能要求自组织模糊规则,并利用梯度下降算法在线调整模糊神经网络的参数和权值,从而提高了控制系统的精度,消除了滑模控制的抖振。
(5)研究了遗传算法的设计方法。针对参数未知的不确定机器人,提出了一种基于改进遗传算法的模糊神经滑模控制方法。该方法首先利用神经网络对机器人进行数学建模,然后利用遗传算法离线优化模糊神经网络的参数,提高了控制系统的在线学习速度,最后利用梯度算法在线调节模糊神经网络的权值,使控制器参数能够随机器人参数的变化而变化,削弱了滑模控制的抖振。另外,为了解决简单遗传算法的不成熟收敛和收敛速度慢的问题,对实数编码的遗传算法设计了一种自适应遗传变异算法。该算法能有效实现全局优化,提高进化效率,对求解复杂的优化问题具有广泛的适应性。
Adaptability and robustness are the basic control characteristic for the trajectory tracking control of multi-link robots. Sliding mode variable structure control (SMVSC) is an efficient approach because of its strong robustness against disturbances and variation of parameters. However, its application and development are limited by the chattering of the SMVSC systems. In order to alleviate chattering, merging SMVSC with intelligent control are efficient approaches, such as fuzzy sliding mode control, neural sliding mode control, fuzzy neural sliding mode control based on genetic algorithm, and so on. These approaches are studied and developed in this paper:
1. Various control structures for multi-link robots are studied and a parallel control structure is presented in this paper. The parallel control structure has two merits: (1) It can take full advantage of the known information about robots, which gives the equivalent control and reduces the learning time of intelligent control for the uncertainty. (2) The advanced techniques, such as embedded systems and computer communication, are applied to increase the response speed of the control systems. The control requirement can be distributed to several microprocessors that are connected each other by control networks. Therefore, every microprocessor has only simple task, fast learning algorithm and control speed. Also, the network control structure has virtues of both centralized control structure and distributed control structure. If one of the control subsystems is damaged, the other control subsystems can continue to work and the reliability of whole control system is developed. This virtue of distributed systems is convenient to detect and remove system trouble. The parallel working ability can develop robustness of the whole system.
2. Designing approaches and general approximate characteristic of fuzzy systems are studied. Two novel control approaches are advanced: one is a fast direct adaptive fuzzy sliding mode control for multi-link robots with model errors; another is a fast indirect adaptive fuzzy sliding mode control for multi-link robots with uncertainty. The fuzzy systems are applied to approximate the unknown parameters instead of estimating them in advance. Therefore, the controller can adaptively adjust its parameters with respect to parameter changes of the controlled system. The chattering of sliding mode control is alleviated without sacrificing the system robustness. The stability of the controller is proved by using Lyapunov direct method. The errors trajectory of the control systems is analyzed in detail. It is proved that the system errors are only relative with the approaching errors of fuzzy systems and independent of the disturbances. So the control systems have small stable errors and strong robustness.
3. Designing and learning approaches of neural networks are studied. A novel global neural sliding mode control based on radial basic function is proposed for multi-link robots with model errors and uncertain disturbances. The neural networks can learn the upper limit of model errors and uncertain disturbances. So the chattering of sliding mode control is reduced. The cost function of neural networks is deduced according to Lyapunov stable theory, which can ensure that the control systems are stable.
4. The fuzzy neural networks which have both merits of fuzzy control and neural networks control are studied. A sliding mode controller based on self organizing fuzzy neural networks is designed. The controller combines the structure learning and parameters learning. The fuzzy rules are selected by competing method according to the system required accuracy. The controller parameters are adjusted by gradient descent algorithm. So the chattering of sliding mode control is eliminated. The cost function of neural networks is deduced according to Lyapunov stable theory, which ensures that the control systems are stable.
5. Genetic algorithm is studied and applied to a fuzzy neural sliding mode controller for multi-link robots without known information. The designed steps of the control system are presented. At first, the controlled system mathematic model must be identified by a neural network. Then, genetic algorithm is applied to train the fuzzy neural networks out systems according to the system mathematic model. At last, the fuzzy neural networks are applied to real time systems which adaptively adjust parameters in systems by gradient descent algorithm with respect to the controlled system parameters. So the chattering of sliding mode control is eliminated without sacrificing its robustness. In order to avoid the slow convergence and the immature convergence of genetic algorithm, an adaptive genetic algorithm is proposed, which can realize to optimize the whole parameters range, develop the optimizing efficiency, and be suitable to complex control systems.
引文
1丁学恭.机器人控制研究.浙江大学出版社. 2006.
2孙富春,孙增圻,张钹.机械手神经网络稳定自适应控制的理论与方法.高等教育出版社. 2005.
3 Young K K D. A variable structure model following control design for robotics applications. IEEE Journal of Robotics and automation. 1988, 4(5): 556-561.
4 Y. Yildiz, A. Sabanovic, K.Abidi. Sliding mode neuro controller for uncertain systems. IEEE Trans on industry and electronics. 2007, 54(3): 1676-1684.
5 H. Hu, P. Y. Woo. Fuzzy supervisory sliding mode and neural network control for manipulators. IEEE Trans on industry and electronics. 2006, 53(3): 929-940.
6 E. M. Jafarov, M. N. A. Parlakc?, Y.Istefanopulos. A new variable structure PID-Controller design for robot manipulators. IEEE Trans on control systems technology. 2005, 1(13): 122-130.
7 V. Santibanez, R. Kelly, M. A. Llama. A novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators. IEEE Trans on fuzzy systems. 2005, 3(13): 362-372.
8 LIN C K. Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks. IEEE Transactions on Fuzzy Systems, 2006, 14(6):849-859
9 Utkin V I. Variable structure systems with sliding modes. IEEE Transactions Automatic Control. 1977, 22(2): 212-222.
10刘金琨.滑模变结构控制MATLAB仿真.清华大学出版社. 2005.
11王立新.模糊系统与模糊控制教程.清华大学出版社. 2003.
12佟绍成.非线性系统的自适应模糊控制.科学出版社. 2006.
13魏海坤.神经网络结构设计的理论与方法.国防工业出版社. 2005.
14周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.清华大学出版社. 2005.
15李敏强,寇纪淞,林丹.遗传算法的基本理论与应用.科学出版社. 2002.
16雷英杰,张善文,李续武. MATLAB遗传算法工具箱与应用.西安电子科技大学出版社. 2005.
17蔡自兴.机器人学.清华大学出版社. 2000.
18 V.I.Utkin. Variable Structure Systems with Sliding Modes. IEEE Transactions on Automatic Control. 1977, 2(22): 212-221.
19张昌凡,何静.滑模变结构的智能控制理论与应用研究.科学出版社. 2005.
20 O. M. E. EI-Ghezawi, A. S. I. Zinober, S. A. Billings. Analysis and design of variable structure systems using a geometric approach. International Journal Control, 1983, 38(3): 657-671.
21 K. K. D. Young. A variable structure model following control design for robotics applications. IEEE Journal of Robotics and automation, 1988, 4(5): 556-561.
22 C. M. Dorling. Two approaches to hyperplane design in multivariable structure control systems. International Journal Control, 1986, 44(1): 65-82.
23 X. H. Yu,Z. H. Man. Fast Terminal Sliding-Mode Control Design for Nonlinear Dynamical Systems. IEEE transaction on Circuits and Systems-I: Fundamental theory and applications,2002, 49(2): 262-264.
24 S. H. Yu, X. H. Yu. Robust Global Terminal Sliding Mode Control of SISO Nonlinear Uncertain Systems. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, 2000: 2198-2203.
25 Y. Feng, X. H. Yu, Z. H. Man. Non-singular terminal sliding mode control of rigid manipulators. Automatica. 2002, 38: 2159-2167.
26 V. I. Utkin, J. X, Shi. Integral Sliding Mode in Systems Operating under Uncertainty Conditions. Proceedings of the 35th IEEE Conference on Decision and Control, Japan, 1996: 4591-4596.
27 J. H. LEE, P. E. Allaire, Gang Tao, et al. Integral Sliding Mode Control of a Magnetically Suspended Balance Beam: Analysis, Simulation, and Experiment. IEEE Transaction on Mechanical, 2001, 6(3): 338-346.
28 Z. M. Chen, J. G. Zhang, J. C. Zeng. Integral Sliding Mode Variable Structure Control Based on Fuzzy Logic. Proceedings of the 3th World Congress on Intelligent Control and Automation, China, 2000: 3009-3012.
29 Y. S. Lu, J. S. Chen. Design of a global sliding mode controller for motor drive with bounded control. International Journal of control. 1995; 62 (5) : 1001-1019.
30 H. S. Choi, Y. H. Park, Y. S. Cho, et al. Global sliding mode control. IEEE control systems magazine. 2001, 21(3): 27-35.
31 H. H. Choi. On the uncertain variable structure systems with bounded controllers. Journal of the Franklin Institute. 2003, 340: 135-146.
32 S. Y. Yi, M. J. Chung. Systematic design and stability analysis of a fuzzy logic controller. Fuzzy Sets and Systems. 1995, 72: 271-298.
33高为炳.变结构控制的理论及设计方法.科学出版社. 1998.
34高为炳.非线性系统的变结构控制.自动化学报. 1989,5(15): 408-415.
35周德文,高存臣,李自强.一种离散变结构控制趋近律.控制与决策. 2008, 3(23): 306-309.
36童克文,张兴,张昱等.基于新型趋近律的永磁同步电动机滑模变结构控制.中国电机工程学报. 2008, 21(28): 102-106.
37 J. X. Xu, Y. J. Pan, T. H. Lee. A gain scheduled sliding mode control scheme using filter techniques with application to multi-link robotic manipulator. Journal of Dynamic Systems, Measurement, and control. 2000, 122: 641-649.
38 G. Bartolini, A. Ferrara, E. Usani. Chattering avoidance by second-order sliding mode control. IEEE Transactions on Automatic Control. 1998, 43(2):241-246.
39 G. Bartolini, A. Ferrara, E. Usai, et al. On Multi-input Chattering-free second-order sliding mode control. IEEE Transactions on Automatic Control. 2000, 9(45): 1711-1717.
40 H. Liu. Smooth sliding mode control of uncertain systems based on a prediction error. International Journal of Robust and Nonlinear control. 1997, 7(4): 353-372.
41 G. C. Hwang, S. Cheng. A Stability Approach to Control Design for Nonlinear Systems. Fuzzy Sets and Systems. 1992, 48(3): 279-287.
42 Q. P. Ha, Q. H. Nguyen, D. C. Rye, et al. Fuzzy sliding-mode controller with application. IEEE Transaction on Industrial Electronics. 2001, 1(48): 38-46.
43朴营国,张俊星,张化光.基于模糊逻辑的一类非线性系统直接自适应控制.控制理论与应用. 2001, 1(18): 45-50.
44达飞鹏,宋文忠.基于输入输出模型的模糊神经网络滑模控制.自动化学报.2000,26(1):136-139.
45 Y. J. Huang, T. C. Kuo, S. H. Chang. Adaptive Sliding-Mode Control for Nonlinear Systems With Uncertain Parameters. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2008, 2(38): 534-539.
46 R. J. Wai, K. H. Su. Adaptive enhanced fuzzy sliding-mode control for electrical servo drive. IEEE Transactions on Industrial Electronics. 2006, 2(53): 569-580.
47 J. M. Zhang, R. H. Li, P. A. Zhang. Stability analysis and systematic design of fuzzy control systems. Fuzzy Sets and Systems. 2001, 1(120): 65-72.
48 S. C. Tan, Y. M. Lai, C. K. Tse. Indirect Sliding Mode Control of Power Converters Via Double Integral Sliding Surface. IEEE Transactions on Power Electronics. 2008, 2(23): 600-611.
49吴玉香,王灏,毛宗源,等.机器人轨迹跟踪的间接自适应模糊控制.控制理论与应用, 2004, 21 (5) : 748 - 756.
50 I. Boiko, L. Fridman, A. Pisano, et, al. Analysis of Chattering in Systems With Second-Order Sliding Modes. IEEE Transactions on Automatic Control. 2007, 11(52): 2085-2102.
51 R. Shahnazi, H. M. Shanechi, N. Pariz. Position Control of Induction and DC Servomotors: A Novel Adaptive Fuzzy PI Sliding Mode Control. IEEE Transaction on Energy Conversion. 2008, 1(23): 138-147.
52 R.M. Hirschorn. Generalized Sliding-Mode Control for Multi-Input Nonlinear Systems. IEEE Transactions on Automatic Control. 2006, 9(51): 1410-1422.
53 G. Bartolini, E. Punta, T. Zolezzi. Approximability Properties for Second-Order Sliding Mode Control Systems. IEEE Transactions on Automatic Control. 2007, 10(52): 1813-1825.
54 A. Levant, L. Alelishvili. Integral High-Order Sliding Modes. IEEE Transactions on Automatic Control. 2007, 7(52): 1278-1282.
55 I. Boiko, L. Fridman, A. Pisano, et al. Performance Analysis of Second-Order Sliding-Mode Control Systems With Fast Actuators. IEEE Transactions on Automatic Control. 2007, 6(52): 1053-1059.
56 D. W. C. Ho, Y. G. Niu. Robust Fuzzy Design for Nonlinear Uncertain Stochastic Systems via Sliding-Mode Control. IEEE Transactions on Fuzzy Systems. 2007, 3(15): 350-358.
57 R. Hirschorn. Sliding-Mode Control Variations. IEEE Transactions on Automatic Control. 2007, 3(52): 468-480.
58 R. J. Wai. Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique. IEEE Transactions on Industrial Electronics. 2007, 1(54): 586-594.
59 F. Betin, A. Sivert, A. Yazidi, et al. Determination of Scaling Factors for Fuzzy Logic Control Using the Sliding-Mode Approach: Application to Control of a DC Machine Drive. IEEE Transactions on Industrial Electronics. 2007, 1(54): 296-309.
60 M. Ertugrual, O.Kaynak. Neuro sliding mode control of robotic manipulators. Mechatronics, 2000, 10: 239-263.
61 Mu Xiaojiang, Chen Yangzhou. Neural sliding mode control for multi-link robots. Control and Decision Conference. 2008: 3513-3517.
62 J. Z. Peng, Y. N. Wang, W. Sun, et al. A Neural Network Sliding Mode Controller with Application to Robotic Manipulator. The Sixth World Congress on Intelligent Control and Automation. 2006, 1: 2101-2105.
63 C. M. Lin, L. Y. Chen, C. H. Chen. RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology. IEEE Transactions on Neural Networks. 2007, 3(18): 708-720.
64 F. J. Lin, L. T. Teng, P. H. Shieh. Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System. IEEE Transactions on Industrial Electronics. 2007, 3(54): 1752-1762.
65 K. Abidi, A. Sabanovic. Sliding-Mode Control for High-Precision Motion of a Piezostage. IEEE Transactions on Industrial Electronics. 2007, 1(54): 629-637.
66 V. Giordano, A. V. Topalov, O. Kaynak, et al. Sliding-mode approach for on-line neural identification of robotic manipulators. Asian Control Conference. 2004, 3: 2060-2065.
67 A. V. Topalov, G. L. Cascella, V. Giordano, et al. Sliding Mode Neuro-Adaptive Control of Electric Drives. IEEE Transactions on Industrial Electronics. 2007,1(54): 671-679.
68 M. G. Zhang, Y. W. Chen, P. Wang, et al. Adaptive sliding mode control using RBF neural network for nonlinear system. 2008 International Conference on Machine Learning and Cybernetics. 2008, 4: 1860-1865.
69 D. muoz, D. Sbarbaro. An adaptive sliding-mode controller for discrete nonlinear systems. IEEE Transactions on Industrial Electronics. 2000, 47 (3): 574-581.
70 R.Garcia-Rodriguez, E. Dean-Leon, V. Parra-Vega, et al. An adaptive neural network controller for visual tracking of constrained robot manipulators. American Control Conference. 2005, 5: 3694-3700.
71 A.G. Ak, G. Cansever. Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator. 2006 IEEE Conference on Cybernetics and Intelligent Systems. 2006: 1-6.
72 F. J. Lin, P. H. Shen. Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System. IEEE Transactions on Industrial Electronics. 2006, 4(53): 1209-1225.
73 K. H. Cheng, C. F. Hsu, C. M. Lin, et al. Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions. IEEE Transactions on Industrial Electronics. 2007, 3(54) : 1528-1536.
74 A. G. Ak, G. Cansever. Three Link Robot Control with Fuzzy Sliding Mode Controller Based on RBF Neural Network. IEEE International Symposium on Intelligent Control. 2006: 2719-2724.
75 C. L. Hwang, L. J. Chang, Y. S. Yu. Network-Based Fuzzy Decentralized Sliding-Mode Control for Car-Like Mobile Robots. IEEE Transactions on Industrial Electronics. 2007, 1(54): 574-585.
76 G. G. Parma, R. D. MenezesB, A. P. Braga. Sliding mode algorithm for training multilayer artificial neural networks. Electronics Letters, 1998, 34 (1) : 97-98.
77 G. G. Parma, R. D. MenezesB, A. P. Braga. Neural networks learning with sliding mode control: the sliding mode back propagation algorithm. International Journal of Neural System, 1999, 9(3) : 187-193.
78 A. CostaM. Training neural networks with a multi-objective sliding mode control algorithm. Neuro computing, 2003, 51: 467-473.
79 M. Efe, O. Kaynak, B. M. Wilamowski. Stable training of computationally intelligent systems by using variable structure systems technique. IEEE Transactions on Industrial Electronics, 2000, 47 (2): 488-495.
80 S. J. Huang, K. S. Huang, K. C. chiou. Development and application of a novel radial basis function sliding mode controller. Mechatronics. 2003, 13: 313-329.
81 G. Leng, G. Prasad, T. M. McGinnity. An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Networks. 2004, 10(17): 1477-1493.
82 T. J. Procky, E. H. Mamdani. A Linguistic Self-oranizing Process Controller. Automatica.1979, 15(1):15-30.
83 J.S. Shieh, D.A. Linkens, A.J. Asbury. A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic. Engineering Applications of Artificial Intelligence. 2005, 3(18): 307-316.
84 Junfei Qiao, Huidong Wang. A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling. Neuro computing. 2008, 4(71): 564-569.
85 W. S. Lin, C. H. Tsai. Self-organizing fuzzy control of multi-variable systems using learning vector quantization network. Fuzzy Sets and Systems. 2001, 2(124): 197-212.
86 R. J. Wai, C. M. Lin, C. F. Hsu. Self-organizing fuzzy control for motor-toggle servomechanism via sliding-mode technique. Fuzzy Sets and Systems. 2002, 2(131): 235-249.
87叶其革,王晨皓,吴捷.基于自组织模糊神经网络电力系统稳定器的设计.控制理论与应用. 1999, 5(16): 687-690
88王耀南.智能控制系统------模糊逻辑·专家系统·神经网络控制.湖南大学出版社. 1996.
89白治江.基于遗传算法的模糊系统研究.华东师范大学博士学位论文. 2005.
90周志坚.基于遗传算法的神经模糊技术应用研究.华南理工大学博士学位论文. 1999.
91伍筱菁.基于改进遗传算法寻优的神经网络PID控制及应用.传感器学报. 2006, 3(19): 865-868.
92王建彬,杨宜民,甘璐.基于改进遗传算法的风/光互补发电系统电压无功控制.控制理论与应用. 2008, 1(25): 172-174.
93王清,马广富,弥曼.一种基于遗传算法的神经网络控制方法研究.系统仿真学报.2006,4(18): 1070-1077.
94 G. Leng, T. M. McGinnity, G. Prasad. Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms. IEEE Transactions on Fuzzy Systems. 2006, 6(14): 755-766.
95 G. C. Liao, T. P. Tsao. Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting. IEEE Transactions on Evolutionary Computation. 2006, 3(10): 330-340.
96 F. J. Lin, P. K. Huang, W. D. Chou. Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms. IEEE Transactions on Industrial Electronics. 2007, 3(54): 1449-1461.
97 S. K. Oh, W. Pedrycz, H. S. Park. Genetically optimized fuzzy polynomial neural networks. IEEE Transactions on Fuzzy Systems. 2006, 1(14): 125-144.
98 G. Alpaydin, G. Dundar, S. Balkir. Evolution-based design of neural fuzzy networks using self-adapting genetic parameters. IEEE Transactions on Fuzzy Systems. 2002, 2(10): 211-221.
99 S. B. Roh, W. Pedrycz, S. K. Oh. Genetic Optimization of Fuzzy Polynomial Neural Networks. IEEE Transactions on Industrial Electronics. 2007, 4(54): 2219-2238.
100 C. H. Wang, H. L. Liu, C. T. Lin. Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2001, 3(31): 467-475.
101 M. Srinivas, L. M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24 (4) : 656-667.
102 Y. Q. Zhang, B. Jin, Y. Tang. Granular Neural Networks With Evolutionary Interval Learning. IEEE Transactions on Fuzzy Systems. 2008, 2(16): 309-319.
103 S. K. Oh, W. Pedrycz, B. J. Park. Self-organizing neurofuzzy networks based on evolutionaryfuzzy granulation. IEEE Transactions on Systems, Man and Cybernetics, Part A. 2003, 2(33): 271-277.
104 W.Y.Wang,C. Y. Cheng, Y. G. Leu. An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2004, 1(34): 334-345.
2孙富春,孙增圻,张钹.机械手神经网络稳定自适应控制的理论与方法.高等教育出版社. 2005.
3 Young K K D. A variable structure model following control design for robotics applications. IEEE Journal of Robotics and automation. 1988, 4(5): 556-561.
4 Y. Yildiz, A. Sabanovic, K.Abidi. Sliding mode neuro controller for uncertain systems. IEEE Trans on industry and electronics. 2007, 54(3): 1676-1684.
5 H. Hu, P. Y. Woo. Fuzzy supervisory sliding mode and neural network control for manipulators. IEEE Trans on industry and electronics. 2006, 53(3): 929-940.
6 E. M. Jafarov, M. N. A. Parlakc?, Y.Istefanopulos. A new variable structure PID-Controller design for robot manipulators. IEEE Trans on control systems technology. 2005, 1(13): 122-130.
7 V. Santibanez, R. Kelly, M. A. Llama. A novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators. IEEE Trans on fuzzy systems. 2005, 3(13): 362-372.
8 LIN C K. Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks. IEEE Transactions on Fuzzy Systems, 2006, 14(6):849-859
9 Utkin V I. Variable structure systems with sliding modes. IEEE Transactions Automatic Control. 1977, 22(2): 212-222.
10刘金琨.滑模变结构控制MATLAB仿真.清华大学出版社. 2005.
11王立新.模糊系统与模糊控制教程.清华大学出版社. 2003.
12佟绍成.非线性系统的自适应模糊控制.科学出版社. 2006.
13魏海坤.神经网络结构设计的理论与方法.国防工业出版社. 2005.
14周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.清华大学出版社. 2005.
15李敏强,寇纪淞,林丹.遗传算法的基本理论与应用.科学出版社. 2002.
16雷英杰,张善文,李续武. MATLAB遗传算法工具箱与应用.西安电子科技大学出版社. 2005.
17蔡自兴.机器人学.清华大学出版社. 2000.
18 V.I.Utkin. Variable Structure Systems with Sliding Modes. IEEE Transactions on Automatic Control. 1977, 2(22): 212-221.
19张昌凡,何静.滑模变结构的智能控制理论与应用研究.科学出版社. 2005.
20 O. M. E. EI-Ghezawi, A. S. I. Zinober, S. A. Billings. Analysis and design of variable structure systems using a geometric approach. International Journal Control, 1983, 38(3): 657-671.
21 K. K. D. Young. A variable structure model following control design for robotics applications. IEEE Journal of Robotics and automation, 1988, 4(5): 556-561.
22 C. M. Dorling. Two approaches to hyperplane design in multivariable structure control systems. International Journal Control, 1986, 44(1): 65-82.
23 X. H. Yu,Z. H. Man. Fast Terminal Sliding-Mode Control Design for Nonlinear Dynamical Systems. IEEE transaction on Circuits and Systems-I: Fundamental theory and applications,2002, 49(2): 262-264.
24 S. H. Yu, X. H. Yu. Robust Global Terminal Sliding Mode Control of SISO Nonlinear Uncertain Systems. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, 2000: 2198-2203.
25 Y. Feng, X. H. Yu, Z. H. Man. Non-singular terminal sliding mode control of rigid manipulators. Automatica. 2002, 38: 2159-2167.
26 V. I. Utkin, J. X, Shi. Integral Sliding Mode in Systems Operating under Uncertainty Conditions. Proceedings of the 35th IEEE Conference on Decision and Control, Japan, 1996: 4591-4596.
27 J. H. LEE, P. E. Allaire, Gang Tao, et al. Integral Sliding Mode Control of a Magnetically Suspended Balance Beam: Analysis, Simulation, and Experiment. IEEE Transaction on Mechanical, 2001, 6(3): 338-346.
28 Z. M. Chen, J. G. Zhang, J. C. Zeng. Integral Sliding Mode Variable Structure Control Based on Fuzzy Logic. Proceedings of the 3th World Congress on Intelligent Control and Automation, China, 2000: 3009-3012.
29 Y. S. Lu, J. S. Chen. Design of a global sliding mode controller for motor drive with bounded control. International Journal of control. 1995; 62 (5) : 1001-1019.
30 H. S. Choi, Y. H. Park, Y. S. Cho, et al. Global sliding mode control. IEEE control systems magazine. 2001, 21(3): 27-35.
31 H. H. Choi. On the uncertain variable structure systems with bounded controllers. Journal of the Franklin Institute. 2003, 340: 135-146.
32 S. Y. Yi, M. J. Chung. Systematic design and stability analysis of a fuzzy logic controller. Fuzzy Sets and Systems. 1995, 72: 271-298.
33高为炳.变结构控制的理论及设计方法.科学出版社. 1998.
34高为炳.非线性系统的变结构控制.自动化学报. 1989,5(15): 408-415.
35周德文,高存臣,李自强.一种离散变结构控制趋近律.控制与决策. 2008, 3(23): 306-309.
36童克文,张兴,张昱等.基于新型趋近律的永磁同步电动机滑模变结构控制.中国电机工程学报. 2008, 21(28): 102-106.
37 J. X. Xu, Y. J. Pan, T. H. Lee. A gain scheduled sliding mode control scheme using filter techniques with application to multi-link robotic manipulator. Journal of Dynamic Systems, Measurement, and control. 2000, 122: 641-649.
38 G. Bartolini, A. Ferrara, E. Usani. Chattering avoidance by second-order sliding mode control. IEEE Transactions on Automatic Control. 1998, 43(2):241-246.
39 G. Bartolini, A. Ferrara, E. Usai, et al. On Multi-input Chattering-free second-order sliding mode control. IEEE Transactions on Automatic Control. 2000, 9(45): 1711-1717.
40 H. Liu. Smooth sliding mode control of uncertain systems based on a prediction error. International Journal of Robust and Nonlinear control. 1997, 7(4): 353-372.
41 G. C. Hwang, S. Cheng. A Stability Approach to Control Design for Nonlinear Systems. Fuzzy Sets and Systems. 1992, 48(3): 279-287.
42 Q. P. Ha, Q. H. Nguyen, D. C. Rye, et al. Fuzzy sliding-mode controller with application. IEEE Transaction on Industrial Electronics. 2001, 1(48): 38-46.
43朴营国,张俊星,张化光.基于模糊逻辑的一类非线性系统直接自适应控制.控制理论与应用. 2001, 1(18): 45-50.
44达飞鹏,宋文忠.基于输入输出模型的模糊神经网络滑模控制.自动化学报.2000,26(1):136-139.
45 Y. J. Huang, T. C. Kuo, S. H. Chang. Adaptive Sliding-Mode Control for Nonlinear Systems With Uncertain Parameters. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2008, 2(38): 534-539.
46 R. J. Wai, K. H. Su. Adaptive enhanced fuzzy sliding-mode control for electrical servo drive. IEEE Transactions on Industrial Electronics. 2006, 2(53): 569-580.
47 J. M. Zhang, R. H. Li, P. A. Zhang. Stability analysis and systematic design of fuzzy control systems. Fuzzy Sets and Systems. 2001, 1(120): 65-72.
48 S. C. Tan, Y. M. Lai, C. K. Tse. Indirect Sliding Mode Control of Power Converters Via Double Integral Sliding Surface. IEEE Transactions on Power Electronics. 2008, 2(23): 600-611.
49吴玉香,王灏,毛宗源,等.机器人轨迹跟踪的间接自适应模糊控制.控制理论与应用, 2004, 21 (5) : 748 - 756.
50 I. Boiko, L. Fridman, A. Pisano, et, al. Analysis of Chattering in Systems With Second-Order Sliding Modes. IEEE Transactions on Automatic Control. 2007, 11(52): 2085-2102.
51 R. Shahnazi, H. M. Shanechi, N. Pariz. Position Control of Induction and DC Servomotors: A Novel Adaptive Fuzzy PI Sliding Mode Control. IEEE Transaction on Energy Conversion. 2008, 1(23): 138-147.
52 R.M. Hirschorn. Generalized Sliding-Mode Control for Multi-Input Nonlinear Systems. IEEE Transactions on Automatic Control. 2006, 9(51): 1410-1422.
53 G. Bartolini, E. Punta, T. Zolezzi. Approximability Properties for Second-Order Sliding Mode Control Systems. IEEE Transactions on Automatic Control. 2007, 10(52): 1813-1825.
54 A. Levant, L. Alelishvili. Integral High-Order Sliding Modes. IEEE Transactions on Automatic Control. 2007, 7(52): 1278-1282.
55 I. Boiko, L. Fridman, A. Pisano, et al. Performance Analysis of Second-Order Sliding-Mode Control Systems With Fast Actuators. IEEE Transactions on Automatic Control. 2007, 6(52): 1053-1059.
56 D. W. C. Ho, Y. G. Niu. Robust Fuzzy Design for Nonlinear Uncertain Stochastic Systems via Sliding-Mode Control. IEEE Transactions on Fuzzy Systems. 2007, 3(15): 350-358.
57 R. Hirschorn. Sliding-Mode Control Variations. IEEE Transactions on Automatic Control. 2007, 3(52): 468-480.
58 R. J. Wai. Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique. IEEE Transactions on Industrial Electronics. 2007, 1(54): 586-594.
59 F. Betin, A. Sivert, A. Yazidi, et al. Determination of Scaling Factors for Fuzzy Logic Control Using the Sliding-Mode Approach: Application to Control of a DC Machine Drive. IEEE Transactions on Industrial Electronics. 2007, 1(54): 296-309.
60 M. Ertugrual, O.Kaynak. Neuro sliding mode control of robotic manipulators. Mechatronics, 2000, 10: 239-263.
61 Mu Xiaojiang, Chen Yangzhou. Neural sliding mode control for multi-link robots. Control and Decision Conference. 2008: 3513-3517.
62 J. Z. Peng, Y. N. Wang, W. Sun, et al. A Neural Network Sliding Mode Controller with Application to Robotic Manipulator. The Sixth World Congress on Intelligent Control and Automation. 2006, 1: 2101-2105.
63 C. M. Lin, L. Y. Chen, C. H. Chen. RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology. IEEE Transactions on Neural Networks. 2007, 3(18): 708-720.
64 F. J. Lin, L. T. Teng, P. H. Shieh. Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System. IEEE Transactions on Industrial Electronics. 2007, 3(54): 1752-1762.
65 K. Abidi, A. Sabanovic. Sliding-Mode Control for High-Precision Motion of a Piezostage. IEEE Transactions on Industrial Electronics. 2007, 1(54): 629-637.
66 V. Giordano, A. V. Topalov, O. Kaynak, et al. Sliding-mode approach for on-line neural identification of robotic manipulators. Asian Control Conference. 2004, 3: 2060-2065.
67 A. V. Topalov, G. L. Cascella, V. Giordano, et al. Sliding Mode Neuro-Adaptive Control of Electric Drives. IEEE Transactions on Industrial Electronics. 2007,1(54): 671-679.
68 M. G. Zhang, Y. W. Chen, P. Wang, et al. Adaptive sliding mode control using RBF neural network for nonlinear system. 2008 International Conference on Machine Learning and Cybernetics. 2008, 4: 1860-1865.
69 D. muoz, D. Sbarbaro. An adaptive sliding-mode controller for discrete nonlinear systems. IEEE Transactions on Industrial Electronics. 2000, 47 (3): 574-581.
70 R.Garcia-Rodriguez, E. Dean-Leon, V. Parra-Vega, et al. An adaptive neural network controller for visual tracking of constrained robot manipulators. American Control Conference. 2005, 5: 3694-3700.
71 A.G. Ak, G. Cansever. Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator. 2006 IEEE Conference on Cybernetics and Intelligent Systems. 2006: 1-6.
72 F. J. Lin, P. H. Shen. Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System. IEEE Transactions on Industrial Electronics. 2006, 4(53): 1209-1225.
73 K. H. Cheng, C. F. Hsu, C. M. Lin, et al. Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions. IEEE Transactions on Industrial Electronics. 2007, 3(54) : 1528-1536.
74 A. G. Ak, G. Cansever. Three Link Robot Control with Fuzzy Sliding Mode Controller Based on RBF Neural Network. IEEE International Symposium on Intelligent Control. 2006: 2719-2724.
75 C. L. Hwang, L. J. Chang, Y. S. Yu. Network-Based Fuzzy Decentralized Sliding-Mode Control for Car-Like Mobile Robots. IEEE Transactions on Industrial Electronics. 2007, 1(54): 574-585.
76 G. G. Parma, R. D. MenezesB, A. P. Braga. Sliding mode algorithm for training multilayer artificial neural networks. Electronics Letters, 1998, 34 (1) : 97-98.
77 G. G. Parma, R. D. MenezesB, A. P. Braga. Neural networks learning with sliding mode control: the sliding mode back propagation algorithm. International Journal of Neural System, 1999, 9(3) : 187-193.
78 A. CostaM. Training neural networks with a multi-objective sliding mode control algorithm. Neuro computing, 2003, 51: 467-473.
79 M. Efe, O. Kaynak, B. M. Wilamowski. Stable training of computationally intelligent systems by using variable structure systems technique. IEEE Transactions on Industrial Electronics, 2000, 47 (2): 488-495.
80 S. J. Huang, K. S. Huang, K. C. chiou. Development and application of a novel radial basis function sliding mode controller. Mechatronics. 2003, 13: 313-329.
81 G. Leng, G. Prasad, T. M. McGinnity. An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Networks. 2004, 10(17): 1477-1493.
82 T. J. Procky, E. H. Mamdani. A Linguistic Self-oranizing Process Controller. Automatica.1979, 15(1):15-30.
83 J.S. Shieh, D.A. Linkens, A.J. Asbury. A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic. Engineering Applications of Artificial Intelligence. 2005, 3(18): 307-316.
84 Junfei Qiao, Huidong Wang. A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling. Neuro computing. 2008, 4(71): 564-569.
85 W. S. Lin, C. H. Tsai. Self-organizing fuzzy control of multi-variable systems using learning vector quantization network. Fuzzy Sets and Systems. 2001, 2(124): 197-212.
86 R. J. Wai, C. M. Lin, C. F. Hsu. Self-organizing fuzzy control for motor-toggle servomechanism via sliding-mode technique. Fuzzy Sets and Systems. 2002, 2(131): 235-249.
87叶其革,王晨皓,吴捷.基于自组织模糊神经网络电力系统稳定器的设计.控制理论与应用. 1999, 5(16): 687-690
88王耀南.智能控制系统------模糊逻辑·专家系统·神经网络控制.湖南大学出版社. 1996.
89白治江.基于遗传算法的模糊系统研究.华东师范大学博士学位论文. 2005.
90周志坚.基于遗传算法的神经模糊技术应用研究.华南理工大学博士学位论文. 1999.
91伍筱菁.基于改进遗传算法寻优的神经网络PID控制及应用.传感器学报. 2006, 3(19): 865-868.
92王建彬,杨宜民,甘璐.基于改进遗传算法的风/光互补发电系统电压无功控制.控制理论与应用. 2008, 1(25): 172-174.
93王清,马广富,弥曼.一种基于遗传算法的神经网络控制方法研究.系统仿真学报.2006,4(18): 1070-1077.
94 G. Leng, T. M. McGinnity, G. Prasad. Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms. IEEE Transactions on Fuzzy Systems. 2006, 6(14): 755-766.
95 G. C. Liao, T. P. Tsao. Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting. IEEE Transactions on Evolutionary Computation. 2006, 3(10): 330-340.
96 F. J. Lin, P. K. Huang, W. D. Chou. Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms. IEEE Transactions on Industrial Electronics. 2007, 3(54): 1449-1461.
97 S. K. Oh, W. Pedrycz, H. S. Park. Genetically optimized fuzzy polynomial neural networks. IEEE Transactions on Fuzzy Systems. 2006, 1(14): 125-144.
98 G. Alpaydin, G. Dundar, S. Balkir. Evolution-based design of neural fuzzy networks using self-adapting genetic parameters. IEEE Transactions on Fuzzy Systems. 2002, 2(10): 211-221.
99 S. B. Roh, W. Pedrycz, S. K. Oh. Genetic Optimization of Fuzzy Polynomial Neural Networks. IEEE Transactions on Industrial Electronics. 2007, 4(54): 2219-2238.
100 C. H. Wang, H. L. Liu, C. T. Lin. Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2001, 3(31): 467-475.
101 M. Srinivas, L. M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24 (4) : 656-667.
102 Y. Q. Zhang, B. Jin, Y. Tang. Granular Neural Networks With Evolutionary Interval Learning. IEEE Transactions on Fuzzy Systems. 2008, 2(16): 309-319.
103 S. K. Oh, W. Pedrycz, B. J. Park. Self-organizing neurofuzzy networks based on evolutionaryfuzzy granulation. IEEE Transactions on Systems, Man and Cybernetics, Part A. 2003, 2(33): 271-277.
104 W.Y.Wang,C. Y. Cheng, Y. G. Leu. An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2004, 1(34): 334-345.
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