计算机工程与应用2019，Vol.55Issue(12)：72-76,1,6.DOI:10.3778/j.issn.1002-8331.1809-0273
基于粒子群的后件多项式RBF神经网络算法
Post-Partial Polynomial RBF Neural Network Algorithm Based on Particle Swarm Optimization
摘要
Abstract
RBF(Radial Basis Function)neural network can be well applied in various fields, the key lies in the selection of network model parameter weight, network center value, base width vector and implicit layer node number. The tradi-tional RBF neural network has the disadvantages of low accuracy, easy to fall into local optimal, slow convergence speed and so on. For these problems, the RBF neural network method is optimized by using particle swarm algorithm, that is, the weight value, network center value, and base width vector value of the RBF neural network containing the latter poly-nomial are optimized, and the optimal number of implicit nodes is selected. Then the PSOIRBF neural network is pro-posed. The effectiveness of the proposed algorithm is demonstrated by the simulation of nonlinear controlled objects such as nonlinear models and examples and the analysis of the models.关键词
后件多项式RBF神经网络/粒子群优化/有效性Key words
post-polynomial RBF neural network/ particle swarm optimization/ effectiveness分类
信息技术与安全科学引用本文复制引用
王燕燕,王宏伟..基于粒子群的后件多项式RBF神经网络算法[J].计算机工程与应用,2019,55(12):72-76,1,6.基金项目
国家自然科学基金(No.61863034). （No.61863034）
