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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >A recurrent neural fuzzy controller based on self-organizing improved particle swarm optimization for a magnetic levitation system
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A recurrent neural fuzzy controller based on self-organizing improved particle swarm optimization for a magnetic levitation system

机译：基于自组织改进粒子群算法的磁悬浮系统递归神经模糊控制器

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This paper proposes a recurrent neural fuzzy controller (RNFC) approach based on a self-organizing improved particle swarm optimization (SOIPSO) algorithm used for solving control problems. The proposed SOIPSO algorithm can adaptively determine the number of fuzzy rules and automatically adjust the parameters in an RNFC. The proposed learning algorithm consisted of phases of structure and parameter learning. Structure learning adopts several subswarms to constitute the adjustable variables in fuzzy systems, and an elite-based structure strategy determines the suitable number of fuzzy rules. This paper proposes an improved particle swarm optimization technique, which consists of the modified evolutionary direction operator (MEDO) and traditional PSO techniques. The proposed MEDO method used the EDO and migration operation to improve the search ability of a global solution. Finally, the proposed RNFC approach based on the SOIPSO learning algorithm (RNFC-SOIPSO) was adopted to control a magnetic levitation system. Experimental results demonstrated that the proposed RNFC-SOIPSO model outperforms other models. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译：本文提出了一种基于自组织改进粒子群算法（SOIPSO）的递归神经模糊控制器（RNFC）方法，用于解决控制问题。提出的SOIPSO算法可以自适应地确定模糊规则的数量，并自动调整RNFC中的参数。所提出的学习算法包括结构学习阶段和参数学习阶段。结构学习采用多个子群来构成模糊系统中的可调节变量，并且基于精英的结构策略确定了合适数量的模糊规则。本文提出了一种改进的粒子群优化技术，它包括改进的进化方向算子（MEDO）和传统的PSO技术。提出的MEDO方法使用EDO和迁移操作来提高全局解决方案的搜索能力。最后，基于SOIPSO学习算法（RNFC-SOIPSO）提出的RNFC方法被用于控制磁悬浮系统。实验结果表明，所提出的RNFC-SOIPSO模型优于其他模型。版权所有（c）2014 John Wiley＆Sons，Ltd.

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