title: Initialization by a novel clustering for wavelet neural network as time series predictor.
abstract: The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examples are given to examine the performance of our method as well as Zhang's heuristic initialization approach. The results show that the new approach not only can decide the WNN structure automatically, but also provides superior initial parameter values that make the optimization process more stable and quickly.
authors.affiliation: School of Science, North University of China, Shanxi, Taiyuan 030051, China ; School of Information and Communication Engineering, North University of China, Shanxi, Taiyuan 030051, China.
authors.forename: Rong
authors.initials: R
authors.lastname: Cheng
authors.affiliation: School of Science, North University of China, Shanxi, Taiyuan 030051, China.
authors.forename: Hongping
authors.initials: H
authors.lastname: Hu
authors.affiliation: School of Science, North University of China, Shanxi, Taiyuan 030051, China ; School of Information and Communication Engineering, North University of China, Shanxi, Taiyuan 030051, China.
authors.forename: Xiuhui
authors.initials: X
authors.lastname: Tan
authors.affiliation: School of Science, North University of China, Shanxi, Taiyuan 030051, China.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Bai
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D013997:Time Factors; D058067:Wavelet Analysis