基于支持向量机的T-S模糊系统辨识

Yanli Deng, Jun Wang, Xiaodan Yan
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引用次数: 0

摘要

模糊系统建模与识别存在模型构建复杂、维数缺乏、泛化能力差、实时性差等问题。为了解决这些问题,本文引入了模糊系统建模的支持向量机(SVM)。然后采用误差反向传播训练算法(BP算法)对参数进行优化。实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
T-S fuzzy system identification based on support vector machine
There are some problems in fuzzy system for modeling and identification, such as complexity of model construction, curse of dimensionality, poverty of generalization and error of real-time. To deal with these problems, support vector mechanism (SVM) for fuzzy system modeling has been introduced in this paper. And then the parameters have been optimized by error back-propagation training algorithm (BP algorithm). Experimental results demonstrate the effectiveness of the method.
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