[论文解读] Using Artificial Intelligence Models in System Identification

Wesam Elshamy|arXiv (Cornell University)|Feb 28, 2013

Neural Networks and Applications参考文献 137被引用 3

一句话总结

本文提出改进的遗传算法（GA）和粒子群优化（PSO）用于感应电动机的系统辨识，引入了多倍体GA和基于俱乐部的PSO（C-PSO）动态邻域结构。C-PSO在多峰参数估计中的收敛速度和精度方面显著优于标准PSO和GA，展现出在减少误差的同时更优地识别电机参数的性能。

ABSTRACT

Artificial Intelligence (AI) techniques are known for its ability in tackling problems found to be unyielding to traditional mathematical methods. A recent addition to these techniques are the Computational Intelligence (CI) techniques which, in most cases, are nature or biologically inspired techniques. Different CI techniques found their way to many control engineering applications, including system identification, and the results obtained by many researchers were encouraging. However, most control engineers and researchers used the basic CI models as is or slightly modified them to match their needs. Henceforth, the merits of one model over the other was not clear, and full potential of these models was not exploited. In this research, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods, which are different CI techniques, are modified to best suit the multimodal problem of system identification. In the first case of GA, an extension to the basic algorithm, which is inspired from nature as well, was deployed by introducing redundant genetic material. This extension, which come in handy in living organisms, did not result in significant performance improvement to the basic algorithm. In the second case, the Clubs-based PSO (C-PSO) dynamic neighborhood structure was introduced to replace the basic static structure used in canonical PSO algorithms. This modification of the neighborhood structure resulted in significant performance of the algorithm regarding convergence speed, and equipped it with a tool to handle multimodal problems. To understand the suitability of different GA and PSO techniques in the problem of system identification, they were used in an induction motor's parameter identification problem. The results enforced previous conclusions and showed the superiority of PSO in general over the GA in such a multimodal problem.

研究动机与目标

• 为利用计算智能技术解决电气机器中多峰系统辨识的挑战。
• 通过引入受生物冗余启发的多倍体，改进传统遗传算法（GA）的性能。
• 通过用动态俱乐部结构替代静态邻域，提升粒子群优化（PSO）的性能。
• 在真实世界多峰条件下，评估并比较改进GA与PSO在感应电动机参数辨识中的表现。
• 确定哪种计算智能技术在复杂系统辨识任务中具备更优的收敛速度、准确性和鲁棒性。

提出的方法

• 改进的GA通过引入冗余遗传物质实现多倍体，以提升搜索空间中的多样性与探索能力。
• C-PSO采用动态的、基于俱乐部的邻域结构，提升信息流动性和局部搜索能力。
• 该算法基于真实电动机数据应用于感应电动机参数辨识问题，使用模型误差作为适应度评估标准。
• 适应度评估采用测量值与仿真电动机响应之间最小二乘误差度量。
• 参数估计在由物理电动机参数范围定义的有界搜索空间内通过优化完成。
• 对五种优化器（标准GA、多倍体GA、标准PSO、全知情PSO和C-PSO）在收敛速度、误差和鲁棒性方面的表现进行了比较。

实验结果

研究问题

• RQ1 在GA中引入多倍体是否能提升多峰系统辨识中的收敛速度和解质量？
• RQ2 PSO中采用如C-PSO的动态邻域结构是否能提升收敛速度并避免局部极小值？
• RQ3 在多峰、非凸适应度景观下，改进的GA与PSO在感应电动机参数辨识中的表现如何比较？
• RQ4 C-PSO相较于标准PSO和GA在收敛速度和参数估计准确性方面的相对表现如何？
• RQ5 所提出的C-PSO拓扑结构是否能有效应对感应电动机参数辨识的多峰特性？

主要发现

• 采用动态邻域结构的基于俱乐部的PSO（C-PSO）在收敛速度和解的准确性方面显著优于标准PSO和GA。
• 多倍体GA在性能上未表现出明显优于标准GA的改善，表明在此情境下冗余遗传物质的益处有限。
• C-PSO在估计参数的平均百分比偏差最低，大多数电动机参数的偏差值低于1.5%。
• 标准PSO和GA表现出较慢的收敛速度和较高的误差率，尤其在存在多个局部最优解时。
• C-PSO拓扑结构在多峰优化景观中展现出更强的逃离局部极小值并收敛至全局最优解的能力。
• 结果证实，C-PSO更适合用于复杂、多峰的系统辨识任务，如感应电动机参数辨识。