SMALL DYNAMIC UNIVERSE ALGORITHM OF FUZZY NEURAL NETWORK IDENTIFIER

机译：模糊神经网络识别器的小动力学通用算法

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This paper represents a small dynamic universe algorithm for achieving both high precision and real-time performance of nonlinear identification by fuzzy neural network. Two influence factors of identification precision, membership function type and numbers of fuzzy subset in the input space, are analysed with given network structure. Then a small dynamic universe algorithm is derived. Different with common fuzzification procedure, algorithm employs methods of shifting a small operating window in the input workspace to generate the initial values of premise fuzzification parameters and reducing the numbers of fuzzy subsets in the windows. So a contradiction between the high precision and real-time performance is avoided. Simulation results of nonlinear identification demonstrate effectiveness of proposed algorithm.

机译：本文提出了一种小型动态宇宙算法，可通过模糊神经网络同时实现高精度和实时的非线性识别性能。利用给定的网络结构，分析了识别精度的两个影响因素，隶属函数类型和输入空间中模糊子集的数量。然后推导了一个小的动态宇宙算法。与常见的模糊化过程不同，该算法采用在输入工作空间中移动一个小的操作窗口以生成前提模糊化参数的初始值并减少窗口中模糊子集数量的方法。因此避免了高精度与实时性能之间的矛盾。非线性辨识的仿真结果证明了该算法的有效性。

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来源
《International Federation of Automatic Control(IFAC) Workshop on New Technologies for Automation of Metallurgical Industry; 20031011-20031013; Shanghai; CN》|2003年|P.333-337|共5页
会议地点 Shanghai(CN)
作者
Xu Da-Ping; Yang Xi-Yun; Dong Ping; Zhang Bin;
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作者单位

Department of automation, North China Electric Power University, Beijing 102206, People Republic of China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类冶金工业;
关键词
fuzzy neural network; identification; nonlinear; real-time; small dynamic universe;

机译：模糊神经网络辨识非线性实时小动态宇宙;

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SMALL DYNAMIC UNIVERSE ALGORITHM OF FUZZY NEURAL NETWORK IDENTIFIER

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