Paper Title:
Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm
  Abstract

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.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
1328-1332
DOI
10.4028/www.scientific.net/AMM.48-49.1328
Citation
Q. F. Tang, L. Zhao, R. B. Qi, H. Cheng, F. Qian, "Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm", Applied Mechanics and Materials, Vols. 48-49, pp. 1328-1332, 2011
Online since
February 2011
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