Self-tuning fuzzy controller design using genetic optimisation and neural network modelling

D.T. Pham, D. Karaboga
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引用次数: 26

Abstract

This article describes a new adaptive fuzzy logic control scheme. The proposed scheme is based on the structure of the self-tuning regulator and employs neural network and genetic algorithm techniques. The system comprises two main parts: on-line process identification and fuzzy logic controller modification using the identified model. A recurrent neural network performs the identification and a genetic algorithm obtains the best process model and evolves the best controller design. The paper presents simulation results for linear and non-linear processes to show the effectiveness of the proposed scheme.

基于遗传优化和神经网络建模的自整定模糊控制器设计
本文提出了一种新的自适应模糊逻辑控制方案。该方案以自整定调节器的结构为基础,采用神经网络和遗传算法技术。该系统包括两个主要部分:在线过程辨识和利用辨识模型对模糊控制器进行修改。递归神经网络进行辨识,遗传算法得到最佳过程模型并演化出最佳控制器设计。本文给出了线性和非线性过程的仿真结果,以证明该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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