用于系统辨识的模糊小波神经网络模型

2009 Ninth International Conference on Intelligent Systems Design and Applications Pub Date : 2009-11-30 DOI:10.1109/ISDA.2009.96

Sevcan Yilmaz, Y. Oysal

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引用次数: 7

摘要

针对系统辨识问题，提出了一种模糊小波神经网络模型。该模型是在传统的Takagi-Sugeno-Kang (TSK)模糊系统的基础上，用具有时频局部化特性的小波基函数代替模糊规则的结果部分得到的。我们在每条规则的后续部分使用了墨西哥帽小波的径向函数。采用基于准牛顿方法的快速梯度算法求解模型中未知参数的最优值。最后给出了文献中一些基准问题的仿真结果，说明了该模型的有效性。

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A Fuzzy Wavelet Neural Network Model for System Identification

In this paper, a fuzzy wavelet neural network model is proposed for system identification problems. The proposed model is obtained from the traditional Takagi-Sugeno-Kang (TSK) fuzzy system by replacing the consequent part of fuzzy rules with wavelet basis functions that have time-frequency localization properties. We use a radial function of Mexican Hat wavelet in the consequent part of each rule. A fast gradient algorithm based on quasi-Newton methods is used to obtain the optimal values for unknown parameters of the model. Simulation results of some benchmark problems in the literature are also given to illustrate the effectiveness of the model.

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来源期刊

2009 Ninth International Conference on Intelligent Systems Design and Applications

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