矢量场测量系进化算法优化研究
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摘要
本论文所研究题目源自哈尔滨市科技局攻关项目“巨磁电阻式精密电子罗盘样机研制”的引申研究。矢量场测量系的优化设计是许多测量装置经常遇到的基本问题，对提高测量精度至关重要，通过算法实现优化是目前最有效的途径。国外研究者较多采用递推最小二乘法、卡尔曼滤波、极大似然估计等方法解决这一问题。本文首次提出利用进化算法技术反演矢量场测量系的正交及配准（对准）误差的方法。“进化算法”对于非线性或不连续多峰函数的优化问题以及无解析表达式的目标函数的优化问题有明显的功效。
本文针对进化算法中的遗传算法和粒子群算法进行了比较深入的研究，提出了具有自主运行能力的进化算法，为离线运行创造了条件，并对其在矢量场测量系校正和配准应用进行了研究，本文所研究的测量系校正与配准方法还有一个特点，即不借助其它精度更高的测量仪器，所有校正与配准依据都由测量系自身提供。本文主要工作如下：
1、提出了自进化遗传算法及分步自进化遗传算法。虽然遗传算法主要进化机制流程模仿了生物进化过程，但其中最敏感的几个进化参数却是由人工给定，这使算法的结果存在一定的偶然性或主观性，同时这样的过程也摆脱不了对人工参与的依赖。如果没有超自然力量的存在，生物进化的内在机制自身也将是不断进化的。自进化遗传将遗传与进化的机制延续到遗传算法自身参数的确定过程中，并将算法自身所涉及的参数在某种意义下视为被寻优变量，使算法通过不断的遗传与进化，自动产生算法参数并同时得出被寻优问题的“最优解”，算法的解更能体现遗传与进化的特质，结果的客观性更强。
2、提出了一种自适应改进PSO（Particle Swarm Optimization）算法。新算法具有自学习特性，控制参数相对较少，同时减少了参数控制的复杂性，能够提高运行过程中特别是迭代后期微粒群的多样性，改进的算法在寻优过程中具有明显的抗早熟能力和更高的收敛精度。
3、提出并建立了用于三轴测量系正交校正问题的进化算法优化模型和若干通用适应度函数。依据将测量系映射到理想正交系的原理和映射后各个矢量的模相等的原则，建立了测量系误差修正模型。
4、结合进化算法提出了相同量纲测量系间的自配准模型，以及不同量纲测量系间的自配准模型，并推导出两种适应度函数，用于测量系进化寻优算法。所谓“自配准”是指不借助其他测量装置进行的一种标定过程。
本文的研究工作为利用遗传算法及粒子群算法实现测量系校正及配准及在线或准在线工作模式提供了可行方案。
This research is from extension of the Harbin Municipal Science and Technology re-search project "Giant Magneto-resistive precision electronic compass ". Optimization for theVector field measurement system is often needed by many measuring devices. Optimizingwith algorithm is the most effective way now. More foreign researchers using recursive leastsquares, Kalman filtering,maximum likelihood estimation and other methods to solve thisproblem. In this paper, the method is first put forward that using computational intelli-gence techniques solving the orthogonal correction and alignment problem for vectormeasurements.
This paper is mainly about the evolutionary algorithms, especially the com-mon geneticalgorithm and particle swarm optimization and the application in the calibration and align-ment of a vector field measurement system, including:
1．A concept of self-evolutionary genetic algorithm is proposed. Since the genetic algo-rithm was proposed, its major evolutionary mechanism is to imitate the biological evolutionprocess, and the most sensitive evolutionary parameters is given by the artificial, this makesome uncertainties for the algorithm results, particularly it unable to escapethe dependence on human intervention. Now that these disadvantage of the classical geneticalgorithm, the “self-evolution genetic algorithm” expands the genetic and evolutionary me-chanisms to the selecting of the parameters for the algorithm in the optimization process, andthe parameters of the algorithm in some sense deemed to be variables that need to be opti-mized, so the algorithm get rid of the human experience and artificial participation, throughgenetic and evolutionary, the optimizal parameters for the algorithm and the "optimum solu-tion" will be generated automatically. This is an algorithm that fully reflect the mechanism ofgenetic and evolution, compared to the classcal genetic algorithm, the new algorithm can bet-ter embody the genetic and evolutionary characteristics, and the objectivity of the result ismore stronger, the operation is simple, at the same time it does not significantly increase thecomputational complexity.
2. An improved adaptive particle swarm optimization algorithm is proposed. The algo-rithm introduces self-learning factor to particle swarm optimization in the process by dynam-ically adjusting the diversity of population and the optimization direction.
3. On the basis of the principle of mapping the measurement system to the ideal ortho-gonal system, and according to the equalation of each vector modulus after coordinate systemtransformation, the measurement system error correction model is established for the ortho-gonal correction of three axis measuring system.
4. In addition, in order to solve the alignment problem for the measuring systems, thispaper put forward two kinds of new methods: one is used for the same vector measurementsystem, another is used for the different vector measurement system. Applicating theself-evolution genetic algorithm and the improved adaptive particle swarm optimization algo-rithm on the identification for the same vector measurement system transformation matrixparameter, experimental results prove the method was correct and feasible.
These efforts is valuable for verifying the stability of the algorithms, and provides afeasible scheme for the use of genetic algorithms and particle swarm algorithms to correctand to registrate the measurement system in online or quasi-online mode.
本文针对进化算法中的遗传算法和粒子群算法进行了比较深入的研究，提出了具有自主运行能力的进化算法，为离线运行创造了条件，并对其在矢量场测量系校正和配准应用进行了研究，本文所研究的测量系校正与配准方法还有一个特点，即不借助其它精度更高的测量仪器，所有校正与配准依据都由测量系自身提供。本文主要工作如下：
1、提出了自进化遗传算法及分步自进化遗传算法。虽然遗传算法主要进化机制流程模仿了生物进化过程，但其中最敏感的几个进化参数却是由人工给定，这使算法的结果存在一定的偶然性或主观性，同时这样的过程也摆脱不了对人工参与的依赖。如果没有超自然力量的存在，生物进化的内在机制自身也将是不断进化的。自进化遗传将遗传与进化的机制延续到遗传算法自身参数的确定过程中，并将算法自身所涉及的参数在某种意义下视为被寻优变量，使算法通过不断的遗传与进化，自动产生算法参数并同时得出被寻优问题的“最优解”，算法的解更能体现遗传与进化的特质，结果的客观性更强。
2、提出了一种自适应改进PSO（Particle Swarm Optimization）算法。新算法具有自学习特性，控制参数相对较少，同时减少了参数控制的复杂性，能够提高运行过程中特别是迭代后期微粒群的多样性，改进的算法在寻优过程中具有明显的抗早熟能力和更高的收敛精度。
3、提出并建立了用于三轴测量系正交校正问题的进化算法优化模型和若干通用适应度函数。依据将测量系映射到理想正交系的原理和映射后各个矢量的模相等的原则，建立了测量系误差修正模型。
4、结合进化算法提出了相同量纲测量系间的自配准模型，以及不同量纲测量系间的自配准模型，并推导出两种适应度函数，用于测量系进化寻优算法。所谓“自配准”是指不借助其他测量装置进行的一种标定过程。
本文的研究工作为利用遗传算法及粒子群算法实现测量系校正及配准及在线或准在线工作模式提供了可行方案。
This research is from extension of the Harbin Municipal Science and Technology re-search project "Giant Magneto-resistive precision electronic compass ". Optimization for theVector field measurement system is often needed by many measuring devices. Optimizingwith algorithm is the most effective way now. More foreign researchers using recursive leastsquares, Kalman filtering,maximum likelihood estimation and other methods to solve thisproblem. In this paper, the method is first put forward that using computational intelli-gence techniques solving the orthogonal correction and alignment problem for vectormeasurements.
This paper is mainly about the evolutionary algorithms, especially the com-mon geneticalgorithm and particle swarm optimization and the application in the calibration and align-ment of a vector field measurement system, including:
1．A concept of self-evolutionary genetic algorithm is proposed. Since the genetic algo-rithm was proposed, its major evolutionary mechanism is to imitate the biological evolutionprocess, and the most sensitive evolutionary parameters is given by the artificial, this makesome uncertainties for the algorithm results, particularly it unable to escapethe dependence on human intervention. Now that these disadvantage of the classical geneticalgorithm, the “self-evolution genetic algorithm” expands the genetic and evolutionary me-chanisms to the selecting of the parameters for the algorithm in the optimization process, andthe parameters of the algorithm in some sense deemed to be variables that need to be opti-mized, so the algorithm get rid of the human experience and artificial participation, throughgenetic and evolutionary, the optimizal parameters for the algorithm and the "optimum solu-tion" will be generated automatically. This is an algorithm that fully reflect the mechanism ofgenetic and evolution, compared to the classcal genetic algorithm, the new algorithm can bet-ter embody the genetic and evolutionary characteristics, and the objectivity of the result ismore stronger, the operation is simple, at the same time it does not significantly increase thecomputational complexity.
2. An improved adaptive particle swarm optimization algorithm is proposed. The algo-rithm introduces self-learning factor to particle swarm optimization in the process by dynam-ically adjusting the diversity of population and the optimization direction.
3. On the basis of the principle of mapping the measurement system to the ideal ortho-gonal system, and according to the equalation of each vector modulus after coordinate systemtransformation, the measurement system error correction model is established for the ortho-gonal correction of three axis measuring system.
4. In addition, in order to solve the alignment problem for the measuring systems, thispaper put forward two kinds of new methods: one is used for the same vector measurementsystem, another is used for the different vector measurement system. Applicating theself-evolution genetic algorithm and the improved adaptive particle swarm optimization algo-rithm on the identification for the same vector measurement system transformation matrixparameter, experimental results prove the method was correct and feasible.
These efforts is valuable for verifying the stability of the algorithms, and provides afeasible scheme for the use of genetic algorithms and particle swarm algorithms to correctand to registrate the measurement system in online or quasi-online mode.
引文
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