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首页> 外文会议>DSC-vol.73-1; American Society of Mechanical Engineers(ASME) International Mechanical Engineering Congress and Exposition pt.A; 20041113-19; Anaheim,CA(US) >NEURAL NETWORK BASED INTELLIGENT LEARNING OF FUZZY LOGIC CONTROLLER PARAMETERS
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NEURAL NETWORK BASED INTELLIGENT LEARNING OF FUZZY LOGIC CONTROLLER PARAMETERS

机译：基于神经网络的模糊逻辑控制器参数智能学习

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摘要

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to leam and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.
机译：一种有效的模糊逻辑控制器的设计涉及到模糊集参数的优化和规则库的适当选择。在最近的文献中报道了几种使用神经网络架构和遗传算法来学习和优化模糊逻辑控制器的技术。本文提出了利用神经网络的学习能力来学习和优化模糊逻辑控制器参数的方法。模型预测控制（MPC）的概念已用于获得最佳信号，以通过反向传播训练神经网络。所开发的策略已应用于控制倒立摆，并且已对借助神经网络开发的两种不同的模糊逻辑控制器的结果进行了比较。第一个神经网络模拟PD控制器，而第二个控制器基于MPC开发。所提出的方法可以应用于通过动态反向传播在线学习模糊逻辑控制器参数。结果表明，神经模糊方法能够学习规则库并准确识别隶属函数参数。

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