基于LSFD算法的小波模糊神经网络的研究与应用

Research and Application of Wavelet Fuzzy Neural Network Based on LSFD Algorithm

利用小波变换的多分辨率特性构造小波模糊神经网络模型,并应用在非线性系统的辨识上。在参数学习上,给出了模糊微分与李亚普诺夫稳定相结合的新算法—LSFD算法,并与梯度下降法进行了对比。通过仿真,结果表明小波模糊神经网络模型与模糊神经网络、模糊小波神经网络、小波神经网络和神经网络等模型相比,其性能指标最小,收敛速度更快,更加准确。

Using multi-resolution characteristics of the wavelet, the model of wavelet fuzzy neural network is constructed, it is applied for the identification of non-linear system. The new learning algorithm of a combination of Lyapunov stability and fuzzy differential is given, a comparison is done with gradient descent. Compared with models of fuzzy neural network, fuzzy wavelet neural network, wavelet neural network and neural network, the simulations to non-linear system demonstrate that the wavelet fuzzy neural network has the performance index of the minimum, the faster convergent speed, and more accurate.