基于模糊神经模型的RBF控制研究

Changlu Zheng, Jian Fan, M. Fei, Zhinian Gao
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引用次数: 1

摘要

本文提出了一种基于模糊神经网络模型的RBF控制器,该控制器利用现场数据对控制对象进行建模,然后利用该模型对RBF控制器中的高斯基函数的中心值、宽度、隐层到输出层的权值等参数进行调整。此外,还将该控制器应用于循环流化床锅炉床层温度的控制。通过与传统PID控制器的比较,仿真结果表明该控制器具有更短的响应时间和更好的跟踪性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RBF Control Research Based on Fuzzy Neural Model
RBF controller based on fuzzy neural network model is given in this paper, which applies field data to model the control object, and then uses the model to adjust the parameters of gauss basis function in RBF controller, such as the central value, the width, and the weights from hidden layer to output layer. In addition, the controller is applied to control the bed temperature of CFB boilers. By comparison with the traditional PID controllers, the simulation result shows that the given controller has shorter response time and better tracking performance.
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