摘要： 针对标准反向传播(BP)算法收敛速度慢和易陷入局部极值等缺陷，提出一种基于粒子群优化的BP神经网络学习算法。采用标准BP梯度下降法调整权值，利用粒子群优化算法进行网络权值及阈值的修正。将该算法与标准BP算法及传统基于粒子群优化BP网络算法进行仿真比较。实验结果表明，该算法能够克服标准BP算法的缺点，性能优于其他2个BP网络优化模型。

关键词: 神经网络, 反向传播算法, 粒子群优化, 梯度下降法, 函数拟合

Abstract: For the standard Back Propagation(BP) algorithm usually has the limitations of slow convergence and local extreme values, a new BP neural network learning algorithm based on Particle Swarm Optimization(PSO) is proposed. The main idea of the model is to modify weight and threshold using PSO based on the weight adjustments of gradient descent method in BP algorithm. It evaluates the model by using simulation test of five typical complex functions and compares it with other two models including standard BP network and traditional PSO based BP network. Experimental results show that it can overcome the limitations of slow convergence and local extreme values for BP network and perform better than other two kinds of optimized BP network models.

Key words: neural network, Back Propagation(BP) algorithm, Particle Swarm Optimization(PSO), gradient descent method, function fitting

中图分类号: 

• TP312