Simulation study on the parameters optimization of radial basis function neural network based on QPSO algorithm
基于QPSO算法的RBF神经网络参数优化仿真研究

Coping with such limitations of Particle Swarm Optimization(PSO) algorithm as finite sampling space,being easy to run into local optima,a new Radial Basis Function Neural Network(RBF NN) training method based on Quantum-behaved Particle Swarm Optimization(QPSO) algorithm was proposed.A multidimensional vector composed of RBF NN parameters was regarded as a particle in this algorithm to evolve.Then,the feasible sampling space was searched for the global optima.The simulation results show that this learning algorithm has easier computation and more rapid convergence compared with other traditional learning algorithms.And due to the characteristic of the algorithm model,its global convergence ability is better than the one based on PSO.