The Local Linear Adaptive Wavelet Neural Network with Hybrid EP/Gradient Algorithm and Its Application to Nonlinear Dynamic System Identification
The Local Linear Adaptive Wavelet Neural Network with Hybrid EP/Gradient Algorithm and Its Application to Nonlinear Dynamic System Identification
复制标题
混合EP/梯度算法的局部线性自适应小波神经网络及其在非线性动态系统辨识中的应用
DOI：
10.1541/ieejeiss1987.122.7_1194
复制
发表时间：
2002
影响因子：
--
通讯作者：
Y. Sugai
中科院分区：
文献类型：
--
作者：
Ting Wang;Y. Sugai
小波神经网络是一种以非线性小波基函数作为神经元激活函数的网络。本文提出了一种新型的小波神经网络：局部线性自适应小波神经网络。将混合进化规划和梯度下降算法引入到该网络的学习中。该方法引入了模糊神经网络中常用的局部线性模型作为权值，取代了以往小波神经网络中的直接权值。首先利用进化规划算法在参数空间中搜索一个好的区域，然后利用梯度下降算法在该区域中找到一个近似最优解。对非线性动态系统辨识问题的实验结果表明，该混合EP/Gradient算法的神经网络能成功地用少量的小波基函数辨识和描述未知复杂系统的输入输出关系，并优于传统的siginoid激励函数神经网络和已有的直接权值小波神经网络.
Wavelet neural networks are networks employing nonlinear' wavelet basis functions as the activation func tions of the neurons. This paper presents a new type of wavelet-based neural network: the local linear adaptive wavelet neural network. A hybrid evolutionary programming and gradient descent algorithm is introduced to the learning of the proposed network. The local linear models which are used in some neuro fuzzy systems are introduced as powerful weights instead of straightforward weights employed in the previous wavelet neural networks. Training is performed by using the evolutionary programming algorithm at first to search a good region in the parameter space and then employing the gradient descent algorithm to find a near optimal solution in that region. The experiments on a number of nonlinear dynamic system identifica tion problems indicates that the proposed network with the hybrid EP/Gradient algorithm can successfully identify and describe the input/output relationship for an unknown complex system with a small number of wavelet basis functions and compared favorably to the traditional neural networks with the siginoid activation functions and the previous wavelet neural networks with straightforward weights.