BP算法分析与改进

在人工神经网络中,BP神经网络是一种应用广泛的多层前馈神经网络。分析了BP算法的基本原理,指出了BP算法具有收敛速度慢、易陷入局部极小点等缺陷以及这些缺陷产生的根源。针对这些缺陷,通过在标准BP算法中引入变步长法、加动量项法、遗传算法、模拟退火算法等几种方法来优化BP算法。实验结果表明,这些方法有效地提高了BP算法的收敛性,避免陷入局部最小点。

Analysis and Improvements of BP Algorithm

Back-propagation neural network is an extensively applied multi-layer feedforward neural network in artificial neural network.Basic principle of BP algorithm is analyzed firstly.Then some defects such as slow convergence rate and getting into local minimum in BP algorithm are pointed out,and the root of the defects is presented.Finally,in view of these limitations,several methods such as genetic algorithm and simulated annealing algorithm etc.are led to optimize BP algorithm.Experiment results show that these methods increase efficiently the convergence performance of BP algorithm and avoid local minimum.