Identification of Frequency Domain Maximum Likelihood System Using Genetic Algorithm
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摘要: 针对基于变量误差模型EV模型(Errors-in-Variables Model)的传递函数频域最大似然参数估计中存在的初始值以及收敛问题提出了使用浮点遗传算法的改进算法.仿真试验表明,单独使用遗传算法难以得到系统传函的精确估计,传统的非线性数值递推算法在一些情况下容易收敛到局域最小值.将两种算法结合使用,可以有效地克服各自的不足.新算法可以给出系统延迟的初始值的估计.当代价函数存在多个局部最小值时,它仍然能够快速准确地寻找到全局最优点.改进的算法比原算法具有更强的适应性.Abstract: Float point genetic algorithms were used to solve the start value and convergence problems of frequency domain maximum likelihood system identification, based on errors-in-variables model. Simulations showed that it is difficult to get accurate result only using the genetic algorithm, while the traditional non-linear iterative optimization methods may lead to convergence to local minimum in some cases. By taking the advantages and overcoming the defects of the above two methods, an improved algorithm was proposed which can give the start value of delay directly and can find the global minimum precisely within a rather short time, even when cost function has a lot of local minima. In addition, the improved algorithm exhibited broader adaptability than the old ones.
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Key words:
- systems identification /
- parameter estimations /
- linear systems /
- genetic algorithms
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[1] Pintelon R, Schoukens J. Robust identification of transfer functions in the s- and z- domains[J]. IEEE Trans Instrum Meas,1990,39(4):565~573. [2] Schoukens J, Pintelon R. Identification of linear systems a practical guideline to accurate modeling[M].Oxford: Pressman Press, 1991. 221~229. [3] Davis L. The handbook of genetic algorithms[M]. New York: Van Nostrand Reingold, 1991. [4] Michalewicz Z. Genetic algorithms + data structure =evolution programs . New York: Springer-Verlag, 1994. [5] Kristinsson K． System identification and control using genetic algorithms [J]. IEEE Trans Sys Man Cybernetics, 1992, 22(5): 1033~1046. -
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