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Comparative Performance of Intelligent Algorithms for System Identification and Control

机译:系统识别与控制智能算法的比较性能

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This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed based on optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is then implemented, teste4 and its performance assessed for GAs and ANFIS based algorithms. Finally, a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments.
机译:本文介绍了积极振动控制(AVC)系统框架内智能系统识别和控制算法的比较表现。进化遗传算法(气体)和自适应神经模糊推理系统(ANFIS)算法用于制定AVC系统的机制,其中控制器基于使用工厂模型的最佳振动抑制来设计。使用有限差分(FD)方法的横向振动中的柔性光束系统的仿真平台被认为是使用气体和ANFIS的AVC系统的能力。用于ANFIS功能的气体和模糊逻辑工具盒MATLAB GA工具盒用于设计AVC系统。然后实现该系统,测试4及其基于气体和ANFIS的算法评估的性能。最后,通过一组实验呈现并讨论了在实现系统识别和相应的AVC系统中实现系统识别和相应AVC系统的比较性能。

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