A Niche Hierarchy Genetic Algorithms for Learning Wavelet Neural Networks

摘要：

Based on the wavelet neural network (WNN) training algorithms and geometrical structure, a niche hierarchy genetic algorithm was proposed to improve the performance of wavelet networks. This evolutionary algorithm utilizes niche technology and the hierarchical chromosome to encode the structure and parameters of the wavelet network, and combines a genetic algorithm and evolutionary programming to construct and train the network simultaneously through evolution. By the evolutionary algorithm, the structure of WNN can be more reasonable, and the local minimum problem in the training process will be overcome efficiently. The experimental results show that the proposed method for the construction and training of the wavelet network is feasible and effective.

作者: Yaoming LUO Guihua NIE

作者单位: Wuhan University of Technology, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）