区间二型模糊逻辑智能系统的设计
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摘要
在实际应用当中,不确定性是智能系统的固有的一部分,用一种新的处理不完全信息的方法是非常必要的。在传统的模糊系统中使用一型模糊集不能完全处理智能系统当中的不确定性。二型模糊逻辑系统中的二型模糊集能够更好地处理这种不确定性,它可以最大限度的降低不确定性对系统的影响,本文主要研究基于线性和非线性系统的区间二型模糊逻辑智能系统设计,分别给出仿真实验研究,最后给出应用实例及其仿真研究。具体工作如下:
1.介绍模糊逻辑智能系统的发展及应用、线性及非线性系统的发展及应用及仿真的相关知识。
2.研究了基于线性系统的区间二型模糊逻辑智能系统的设计;给出了基于线性系统的区间二型模糊逻辑智能系统的仿真实验;研究了区间二型模糊逻辑控制器在主汽温度系统中的应用,并应用MATLAB进行仿真,仿真结果表明,把所设计的区间二型模糊逻辑系统应用于主汽温的模糊控制之中是合理的,与一型模糊逻辑系统比较,取得了更好的控制效果。
3.研究了基于非线性系统的区间二型模糊逻辑智能系统的设计;给出了基于非线性系统的区间二型模糊逻辑智能系统的仿真实验;研究了区间二型模糊逻辑控制器在淋浴系统中的应用,并进行MATLAB仿真,仿真结果表明,所设计的区间二型模糊逻辑系统应用于淋浴控制系统是合理,与一型模糊逻辑系统比较,取得了更好的控制效果。
Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance.Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems.Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide us with more parameters. This thesis, deals with the design of intelligent systems using interval type-2 fuzzy logic for minimizing the effects of uncertainty. Experimental results include simulations of feedback control systems for linear and non-linear plants using and interval type-2 fuzzy logic controllers. It contains the following contents.
1. It introduces interval type-2 fuzzy logic systems, linear and nonlinear systems, fuzzy control knowledge and the main application.
2. It studies interval type-2 fuzzy logic intelligent system design based on linear systems, research and simulation examples are obtained by MATLAB, and compare the results of interval type-2 fuzzy logic controllers with type-1 fuzzy logic controllers. It shows that the proposed strategy is applied successfully to control the main steam temperature system and the performance of IT2FLC is better than T1FLC.
3. It studies interval type-2 fuzzy logic intelligent system design based on nonlinear systems, research and simulation examples are obtained by MATLAB, and compare the results of interval type-2 fuzzy logic controllers with type-1 fuzzy logic controllers. It shows that the proposed strategy is applied successfully to control the shower system and the performance of IT2FLC is better than T1FLC.
1.介绍模糊逻辑智能系统的发展及应用、线性及非线性系统的发展及应用及仿真的相关知识。
2.研究了基于线性系统的区间二型模糊逻辑智能系统的设计;给出了基于线性系统的区间二型模糊逻辑智能系统的仿真实验;研究了区间二型模糊逻辑控制器在主汽温度系统中的应用,并应用MATLAB进行仿真,仿真结果表明,把所设计的区间二型模糊逻辑系统应用于主汽温的模糊控制之中是合理的,与一型模糊逻辑系统比较,取得了更好的控制效果。
3.研究了基于非线性系统的区间二型模糊逻辑智能系统的设计;给出了基于非线性系统的区间二型模糊逻辑智能系统的仿真实验;研究了区间二型模糊逻辑控制器在淋浴系统中的应用,并进行MATLAB仿真,仿真结果表明,所设计的区间二型模糊逻辑系统应用于淋浴控制系统是合理,与一型模糊逻辑系统比较,取得了更好的控制效果。
Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance.Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems.Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide us with more parameters. This thesis, deals with the design of intelligent systems using interval type-2 fuzzy logic for minimizing the effects of uncertainty. Experimental results include simulations of feedback control systems for linear and non-linear plants using and interval type-2 fuzzy logic controllers. It contains the following contents.
1. It introduces interval type-2 fuzzy logic systems, linear and nonlinear systems, fuzzy control knowledge and the main application.
2. It studies interval type-2 fuzzy logic intelligent system design based on linear systems, research and simulation examples are obtained by MATLAB, and compare the results of interval type-2 fuzzy logic controllers with type-1 fuzzy logic controllers. It shows that the proposed strategy is applied successfully to control the main steam temperature system and the performance of IT2FLC is better than T1FLC.
3. It studies interval type-2 fuzzy logic intelligent system design based on nonlinear systems, research and simulation examples are obtained by MATLAB, and compare the results of interval type-2 fuzzy logic controllers with type-1 fuzzy logic controllers. It shows that the proposed strategy is applied successfully to control the shower system and the performance of IT2FLC is better than T1FLC.
引文
1 Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338-353
2 Mamdani E. H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller.Int.J.Man-Machine Studies, 1974, 7(1): 1-13
3 Mendel J M, Mouzouris G C. Designing Fuzzy Logic Systems. IEEE Trans.on Circuits and Systems- II: Analog and Digital Signal Processing, 1997, 44(11):885-895
4 Mendel J M. Lessons in Estimation Theory for Signal Processing, Communications and Control. Prentice-Hall PTR, Englewood Cliffs, NJ,1995
5 Karnik N N,Mendel J M. Type-2 Fuzzy Logic Systems:Type-Reduction. Proc. IEEE Conference on Systems, 1998, 2(11): 2046-2051
6 Karnik N N, Mendel J M. Application of Type-2 Fuzzy Logic Systems to Forecasting of Time-Series. Information Sciences, 1999, 120(1-4): 89-111
7 Zadeh L A. The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-1. Information Sciences, 1975, 8(3): 199-249
8 Mizumoto M, Tanaka K. Some Properties of Fuzzy Sets of Type-2. Information and Control, 1976, 31(4): 312-340
9 Mizumoto M, Tanaka K. Fuzzy Sets of Type-2 Under Algebraic Product and Algebraic Sum. Fuzzy Sets and Systems, 1981, 5(3): 277-290
10 Karnik.N.N, Mendel.J.M.Operations on Type-2 fuzzy sets, Fuzzy Sets and Systems, 2001, 122 (2): 327-348
11 Mendel J M, John R I. Type-2 fuzzy sets made simple, IEEE Trans. on Fuzzy Systems, 2002, 10(2): 117-127
12 Karnik N N, Mendel J M and Liang Q. Typer-2 Fuzzy Logic Systems. IEEE Trans. on Fuzzy Systems, 1999, 7: 643-658
13 Liang Q, Mendel J M. Interval Type-2 Fuzzy Logic Systems. Proc. FUZZ-IEEE, San Antonio, TX, May 2000, 1(7-10): 328-333
14 Liang Q, Mendel J M. Interval Type-2 Fuzzy Logic Systems:Theory and Design. IEEE Trans. on Fuzzy Systems, 2000, 8: 535-550
15 Mendel J M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, Inc., 2001
16 Mendel J M., John R I and Liu F. Interval Type-2 Fuzzy Logic System Made Simple. IEEE Trans. Fuzzy Syst., 2006, 14(6): 808-821
17 Wu D, Tan W W. Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller, Proc. IEEE Fuzzy conf, Reno, NV, 2005, 8(2): 353-358
18 Mendel J M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. NJ: Prentice-Hall, 2001, 2(1): 555
19 Mendel J M, Wu H. New results about the centroid of an interval type-2 fuzzy set, including the centroid of a fuzzy granule. Inform. Sci., 2007, 177(2): 360-377
20 Castillo O, Melin P. A new approach for plant monitoring using type-2 fuzzy logic and fractal theory, International Journal of General Systems, 2004, 33(2-3): 305-319
21 Castro J R, Castillo O and Martínez L G. Interval Type-2 Fuzzy Logic Toolbox, Journal Engineering Letters, 2007, 15(1): 89-98
22 Mendel J M, Liu F. Super-Exponential Convergence of the Karnik-Mendel Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set. IEEE Transactions on Fuzzy Systems, 2007, 15(2): 309-320
23 Rhee F, Hwang C. Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means, IEEE Transactions on Fuzzy Systems, 2007, 15(1): 107-120
24 Wu H, Mendel J M. Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 2002, 10(5): 622-639
25 Castro J R. Interval Type-2 Fuzzy Logic Toolbox. Proceedings of International Seminar on Computational Intelligence, 2007, 12(3): 100-108
26 Rhee F, Hwang C. An interval type-2 fuzzy perceptron, Proc. 2002 Int. Conf Fuzzy Syst., 2002, 2: 1331-1335
27 Jin Y. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Trans. Fuzzy Syst., 2000, 8(2): 212-221
28 Tao W, Yang C and Shaocheng T. Type-2 Fuzzy Reasoning Model and Algorithms, International Journal of Innovative Computing, Information and Control, 2008, 4(10): 2451-2460
29 Hagras H. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 2004, 12(2): 524-539
30 Ozen T, Garibaldi J M. Effect of type-2 fuzzy membership function shape on modelling variation in human decision making, Proceedings of the FUZZ-IEEE, 2004, 2: 971-976
31 Hagras H. Type-2 FLCs: a new generation of fuzzy controllers, IEEE Computational Intelligence Magazine, 2007, 2(1): 30-43
32 Hagras H. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 2004, 12(4): 524-539
33 Mendel J M. Advances in type-2 fuzzy sets and systems, Information Sciences, 2007, 177: 84-110
34 Li L, Lin W H and Liu H, Type-2 fuzzy logic approach for short-term traffic forecasting, IEE Proceedings of the Intelligent Transport Systems, 2006, 153(1): 33- 40
35 Figueroa J, Posada J, Soriano J, Melgarejo M and Rojas S. A type-2 fuzzy controller for tracking mobile objects in the context of robotic soccer games, Proceedings of FUZZ-IEEE, Reno, NV, 2005 , 5(14): 359-364
36 Karnik N N , Mendel J M. Applications of type-2 fuzzy logic systems to forecasting of time-series, Information Science, 2007, 120(1- 4): 89-111
37 Castillo O, Melin P. Intelligent Systems with Interval Type-2 Fuzzy Logic, International Journal of Innovative Computing, Information and Control, 2008, 4(4): 771-783
38 Martínez R, Castillo O and Aguilar L T. Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms, Information Sciences, 2009, 179(13): 2158-2174
39 Castro J R. Castillo Oscar, Melin Patricia, An interval type-2 fuzzy logic toolbox for control applications, Proc. FUZZ-IEEE, 2007, 7(1): 1-6
40 Leal-Ramírez C, Castillo O and Rodríguez D A. Interval type-2 fuzzy logic system to simulate the environment resources stochasticity affecting the growth of a population, IEEE International Conference on Fuzzy Systems, 2009, 6(2): 726-731
41 Hsiao M Y, Li T-H S. Design of interval type-2 fuzzy logic controller, IEEE International Conference on Systems, Man and Cybernetics, 2006, 8(11): 976-981
42 Liang Q, Mendel J M. Equalization of nonlinear time-varying channels using type-2 fuzy adaptivefilters, IEEE Trans. Fuzzy Syst., 2000, 8(5): 551-563
43 O Castillo and P Melin.Type-2 Fuzzy Logic:Theory and Application, 2008, 5(8): 53-76
44 Tao W, Long L, Application of Type-2 fuzzy logic system in indoor temperature control, ICIC Express Letters An International Journal of Research and Surveys, 2010, 4(5(B)): 1919-1924
45 Tao W, Koucheng B, Jie L, Interval Type-2 fuzzy water level controller and simulation, ICIC Express Letters An International Journal of Research and Surveys, 2011, 5(8(A)): 2505-2510
2 Mamdani E. H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller.Int.J.Man-Machine Studies, 1974, 7(1): 1-13
3 Mendel J M, Mouzouris G C. Designing Fuzzy Logic Systems. IEEE Trans.on Circuits and Systems- II: Analog and Digital Signal Processing, 1997, 44(11):885-895
4 Mendel J M. Lessons in Estimation Theory for Signal Processing, Communications and Control. Prentice-Hall PTR, Englewood Cliffs, NJ,1995
5 Karnik N N,Mendel J M. Type-2 Fuzzy Logic Systems:Type-Reduction. Proc. IEEE Conference on Systems, 1998, 2(11): 2046-2051
6 Karnik N N, Mendel J M. Application of Type-2 Fuzzy Logic Systems to Forecasting of Time-Series. Information Sciences, 1999, 120(1-4): 89-111
7 Zadeh L A. The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-1. Information Sciences, 1975, 8(3): 199-249
8 Mizumoto M, Tanaka K. Some Properties of Fuzzy Sets of Type-2. Information and Control, 1976, 31(4): 312-340
9 Mizumoto M, Tanaka K. Fuzzy Sets of Type-2 Under Algebraic Product and Algebraic Sum. Fuzzy Sets and Systems, 1981, 5(3): 277-290
10 Karnik.N.N, Mendel.J.M.Operations on Type-2 fuzzy sets, Fuzzy Sets and Systems, 2001, 122 (2): 327-348
11 Mendel J M, John R I. Type-2 fuzzy sets made simple, IEEE Trans. on Fuzzy Systems, 2002, 10(2): 117-127
12 Karnik N N, Mendel J M and Liang Q. Typer-2 Fuzzy Logic Systems. IEEE Trans. on Fuzzy Systems, 1999, 7: 643-658
13 Liang Q, Mendel J M. Interval Type-2 Fuzzy Logic Systems. Proc. FUZZ-IEEE, San Antonio, TX, May 2000, 1(7-10): 328-333
14 Liang Q, Mendel J M. Interval Type-2 Fuzzy Logic Systems:Theory and Design. IEEE Trans. on Fuzzy Systems, 2000, 8: 535-550
15 Mendel J M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, Inc., 2001
16 Mendel J M., John R I and Liu F. Interval Type-2 Fuzzy Logic System Made Simple. IEEE Trans. Fuzzy Syst., 2006, 14(6): 808-821
17 Wu D, Tan W W. Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller, Proc. IEEE Fuzzy conf, Reno, NV, 2005, 8(2): 353-358
18 Mendel J M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. NJ: Prentice-Hall, 2001, 2(1): 555
19 Mendel J M, Wu H. New results about the centroid of an interval type-2 fuzzy set, including the centroid of a fuzzy granule. Inform. Sci., 2007, 177(2): 360-377
20 Castillo O, Melin P. A new approach for plant monitoring using type-2 fuzzy logic and fractal theory, International Journal of General Systems, 2004, 33(2-3): 305-319
21 Castro J R, Castillo O and Martínez L G. Interval Type-2 Fuzzy Logic Toolbox, Journal Engineering Letters, 2007, 15(1): 89-98
22 Mendel J M, Liu F. Super-Exponential Convergence of the Karnik-Mendel Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set. IEEE Transactions on Fuzzy Systems, 2007, 15(2): 309-320
23 Rhee F, Hwang C. Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means, IEEE Transactions on Fuzzy Systems, 2007, 15(1): 107-120
24 Wu H, Mendel J M. Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems, IEEE Trans. Fuzzy Syst., 2002, 10(5): 622-639
25 Castro J R. Interval Type-2 Fuzzy Logic Toolbox. Proceedings of International Seminar on Computational Intelligence, 2007, 12(3): 100-108
26 Rhee F, Hwang C. An interval type-2 fuzzy perceptron, Proc. 2002 Int. Conf Fuzzy Syst., 2002, 2: 1331-1335
27 Jin Y. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Trans. Fuzzy Syst., 2000, 8(2): 212-221
28 Tao W, Yang C and Shaocheng T. Type-2 Fuzzy Reasoning Model and Algorithms, International Journal of Innovative Computing, Information and Control, 2008, 4(10): 2451-2460
29 Hagras H. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 2004, 12(2): 524-539
30 Ozen T, Garibaldi J M. Effect of type-2 fuzzy membership function shape on modelling variation in human decision making, Proceedings of the FUZZ-IEEE, 2004, 2: 971-976
31 Hagras H. Type-2 FLCs: a new generation of fuzzy controllers, IEEE Computational Intelligence Magazine, 2007, 2(1): 30-43
32 Hagras H. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 2004, 12(4): 524-539
33 Mendel J M. Advances in type-2 fuzzy sets and systems, Information Sciences, 2007, 177: 84-110
34 Li L, Lin W H and Liu H, Type-2 fuzzy logic approach for short-term traffic forecasting, IEE Proceedings of the Intelligent Transport Systems, 2006, 153(1): 33- 40
35 Figueroa J, Posada J, Soriano J, Melgarejo M and Rojas S. A type-2 fuzzy controller for tracking mobile objects in the context of robotic soccer games, Proceedings of FUZZ-IEEE, Reno, NV, 2005 , 5(14): 359-364
36 Karnik N N , Mendel J M. Applications of type-2 fuzzy logic systems to forecasting of time-series, Information Science, 2007, 120(1- 4): 89-111
37 Castillo O, Melin P. Intelligent Systems with Interval Type-2 Fuzzy Logic, International Journal of Innovative Computing, Information and Control, 2008, 4(4): 771-783
38 Martínez R, Castillo O and Aguilar L T. Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms, Information Sciences, 2009, 179(13): 2158-2174
39 Castro J R. Castillo Oscar, Melin Patricia, An interval type-2 fuzzy logic toolbox for control applications, Proc. FUZZ-IEEE, 2007, 7(1): 1-6
40 Leal-Ramírez C, Castillo O and Rodríguez D A. Interval type-2 fuzzy logic system to simulate the environment resources stochasticity affecting the growth of a population, IEEE International Conference on Fuzzy Systems, 2009, 6(2): 726-731
41 Hsiao M Y, Li T-H S. Design of interval type-2 fuzzy logic controller, IEEE International Conference on Systems, Man and Cybernetics, 2006, 8(11): 976-981
42 Liang Q, Mendel J M. Equalization of nonlinear time-varying channels using type-2 fuzy adaptivefilters, IEEE Trans. Fuzzy Syst., 2000, 8(5): 551-563
43 O Castillo and P Melin.Type-2 Fuzzy Logic:Theory and Application, 2008, 5(8): 53-76
44 Tao W, Long L, Application of Type-2 fuzzy logic system in indoor temperature control, ICIC Express Letters An International Journal of Research and Surveys, 2010, 4(5(B)): 1919-1924
45 Tao W, Koucheng B, Jie L, Interval Type-2 fuzzy water level controller and simulation, ICIC Express Letters An International Journal of Research and Surveys, 2011, 5(8(A)): 2505-2510