使用人工智能模型进行系统辨识
Using Artificial Intelligence Models in System Identification
AI总结：
本研究修改遗传算法和粒子群优化算法以适应系统辨识的多模态问题，并应用于感应电机参数辨识，结果表明粒子群优化算法在多模态问题上优于遗传算法。
摘要：
人工智能技术以其解决传统数学方法难以处理问题的能力而闻名。最近加入这些技术的是计算智能技术，在大多数情况下，它们是受自然或生物启发的技术。不同的计算智能技术已应用于许多控制工程应用，包括系统辨识，许多研究人员获得的结果令人鼓舞。然而，大多数控制工程师和研究人员直接使用基本计算智能模型或稍作修改以满足其需求。因此，一种模型相对于另一种模型的优点并不明确，这些模型的全部潜力也未得到充分利用。 在本研究中，遗传算法和粒子群优化方法这两种不同的计算智能技术被修改以最适合系统辨识的多模态问题。在遗传算法的第一种情况下，通过引入冗余遗传物质，对基本算法进行了扩展，该扩展也受自然启发。这种在生物体中很有用的扩展并未显著提高基本算法的性能。在第二种情况下，引入了基于俱乐部的粒子群优化动态邻域结构，以取代规范粒子群优化算法中使用的基本静态结构。这种邻域结构的修改显著提高了算法在收敛速度方面的性能，并使其具备了处理多模态问题的工具。 为了理解不同遗传算法和粒子群优化技术在系统辨识问题中的适用性，将它们用于感应电机参数辨识问题。结果强化了先前的结论，并表明粒子群优化在这种多模态问题上总体上优于遗传算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
