基于改进BP神经网络的函数逼近性能对比研究

为了正确反映实际应用中经常采用的6种典型BP神经网络的改进算法的非线性函数逼近能力,本文从数学角度详细阐述这6种典型BP神经网络的改进算法的学习过程,简要地介绍MATLAB工具箱中设计BP网络的训练函数,最后在MATLAB环境下设计具体的网络来对指定的非线性函数进行逼近实验,并对这6种典型BP神经网络的改进算法的性能差异进行对比。仿真结果表明,对于中小规模网络而言,LM优化算法逼近性能最佳,其次是拟牛顿算法、共轭梯度法、弹性BP算法、自适应学习速率算法和动量BP算法。

Performance Comparison of Function Approximation Based on Improved BP Neural Network

To accurately reflect the nonlinear function approximation abilities of improved algorithms of six typical BP networks,this paper elaborates on improved algorithm learning processes of the six typical BP networks.And the training function of MATLAB toolbox is briefly introduced which is used for BP network design.Finally a specific network is designed on MATLAB platform to conduct approximation test for a given nonlinear function.At the same time,a comparison between the performance differences of the six BP networks is made.The simulation result indicates that for a small scaled network,LM optimization algorithm has the best approximation ability,followed by quasi-Newton algorithm,conjugate gradient method,resilient BP algorithm,adaptive learning rate algorithm and momentum BP algorithm.