关键词: 小波神经网络, 改进的量子粒子群, 参数组合优化

Abstract: To improve the performance of Wavelet Neural Network (WNN) model in dealing with complex nonlinear problems, concerning the shortcomings of premature convergence, poor late diversity, poor search accuracy of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, a modified quantum-behaved particle swarm algorithm was proposed for WNN training by introducing weighting coefficients, introducing Cauchy random number, improving contraction-expansion coefficient and introducing natural selection at the same time. And then, it replaced the gradient descent method with the modified quantum-behaved particle swarm algorithm, trained the wavelet coefficients and network weights, and then input the optimized combination of parameters into wavelet neural network to achieve the algorithm coupling. The simulation results on three UCI standard datasets show that the running time of the Modified Quantum-behaved Particle Swarm Optimization-Wavelet Neural Network (MQPSO-WNN) was reduced by 11%~43%, while the calculation error was decreased by 8%~57%, compared with wavelet neural network, PSO-WNN and QPSO-WNN. Therefore, the MQPSO-WNN model can approximate the optimal value more quickly and more accurately.

Key words: Wavelet Neural Network (WNN), modified Quantum-behaved Particle Swarm Optimization (QPSO), parameters combination optimization