计算机应用与软件Issue(9)：19-20,99,3.DOI:10.3969/j.issn.1000-386x.2013.09.006
基于粒子群优化与 BP算法的协同神经网络学习方法
COOPERATIVE NEURAL NETWORK LEARNING ALGORITHM BASED ON PARTICLE SWARM OPTIMISATION AND BP NEURAL NETWORK
摘要
Abstract
For the standard BP algorithm usually has the limitations of local extreme values and slow convergence , a cooperative neural network learning method based on particle swarm optimisation ( PSO ) and BP algorithm is proposed in this paper .During the process of network learning, this method makes use of both PSO and BP algorithms simultaneously to carry out the cooperative search of optimal network weight, so that it takes the full advantages of global search property of PSO and back propagation feature of BP algorithm .We apply this algorithm in fitting simulation with four complex functions and compare it with the BP neural network algorithms based on either standard BP network or traditional PSO .Experimental results show that the cooperative algorithm proposed performs better than the traditional BP network optimisation algorithms .关键词
BP算法/粒子群算法/优化/函数拟合/协同算法Key words
BP algorithm/Particle swarm optimisation algorithm/Optimising/Function fitting/Cooperative algorithm分类
信息技术与安全科学引用本文复制引用
江丽,王爱平..基于粒子群优化与 BP算法的协同神经网络学习方法[J].计算机应用与软件,2013,(9):19-20,99,3.基金项目
国家自然科学基金项目（61074071，61104022）。 （）
