A Neural Network Learning Algorithm Based on Hybrid Particle Swarm Optimization
A hybrid learning algorithm based on simplex method and particle swarm optimization is proposed to train the feedforward neural network in this paper. In the given hybrid algorithm the simplex method which has expansion function and contraction function is embedded in the particle swarm optimization as an operator. Through cross-training mode to train neural network, this hybrid algorithm selects limited elitist particles and executes simplex operator for local searching during each generation of particle swarm optimization, which can make the neural network learning approximate to the global optimum region rapidly and find more excellent solution. The simulation experiments show that comparing with some traditional learning methods this hybrid algorithm enhances the convergence speed and training precision, and improves network performance. It is an effective neural network learning method.
feedforward neural network particle swarm optimization simplez method hybrid algorithm
Luo Zaifei Guan Binglei Zhou Shiguan
Academy of Electrics and Information, Ningbo University of Technology, Ningbo 315000
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3255-3259
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）