文献类型：期刊文献

英文题名：Tuning the structure and parameters of a neural network by using cooperative quantum particle swarm algorithm

作者：Tang, Qifeng[1]; Zhao, Liang[1]; Qi, Rongbin[1]; Cheng, Hui[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2011

卷号：48-49

起止页码：1328

外文期刊名：Applied Mechanics and Materials

收录：EI(收录号：20110813675670)

语种：英文

外文关键词：Genetic algorithms - Parameter estimation - Particle swarm optimization (PSO)

摘要：In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures. ? (2011) Trans Tech Publications.