摘要
基于过程神经网络(procedure neural network,PNN)建立了具有高精确度的多步预测模型。针对PNN训练过程复杂的特点,提出了一种基于正交基函数展开和矢量矩免疫算法(vector distance based i mmunealgorithm,VD-IA)相结合的PNN训练方法。根据PNN在三角函数正交基展开形式下的数学模型,推导出适用于VD-IA的优化问题模型,采用一种自适应策略加快了VD-IA的收敛速度。基于Mackey-Glass混沌序列检验了该方法的有效性,将该方法与BP训练方法、改进粒子群优化(i mproved particle swarmopti mization,IPSO)算法进行了对比分析。仿真结果表明,基于VD-IA的PNN训练方法可以获得较优的结果,且获得泛化性能较好的PNN模型。
A multi-steps forecast model possessing high precision based on procedure neural network（PNN） is established.Aiming at the complexity of training PNN,a new algorithm based on combining orthogonal function basis expansion and vector distance based immune algorithm（VD-IA） is proposed.The mathematic model of PNN that is expressed based on orthogonal trigonometric function basis is used to deduce the optimization model suitable to the VD-IA.An adaptive strategy is designed to obtain quick convergence process.The validity of the proposed method is vertified by Mackey-Glass chaotic sequence and is compared with both BP algorithm and improved particle swarm optimization（IPSO） algorithm.Simulation results show that the outstanding results can be obtained by using VD-IA,and the generalization performance of the IA-PNN is also outstanding.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第10期2136-2140,共5页
Systems Engineering and Electronics
基金
"十一五"国防预先研究项目(51317030103)资助课题
关键词
预测模型
过程神经网络
免疫算法
学习算法
函数正交基
forecast model
procedure neural network（PNN）
immune algorithm（IA）
learning algorithm
orthogonal function basis
