查询结果:   李瑞国,张宏立,王雅.基于Hermite神经网络的混沌时间序列预测[J].计算机应用与软件,2016,33(4):268 - 272.
中文标题
基于Hermite神经网络的混沌时间序列预测
发表栏目
算法
摘要点击数
758
英文标题
CHAOTIC TIME SERIES PREDICTION BASED ON Hermite NEURAL NETWORK
作 者
李瑞国 张宏立 王雅 Li Ruiguo Zhang Hongli Wang Ya
作者单位
新疆大学电气工程学院 新疆 乌鲁木齐 830047 新疆大学机械工程学院 新疆 乌鲁木齐 830047    
英文单位
College of Electrical Engineering,Xinjiang University,Urumqi 830047,Xinjiang,China College of Mechanical Engineering,Xinjiang University,Urumqi 830047,Xinjiang,China    
关键词
相空间重构 Hermite神经网络 粒子群算法 混沌时间序列预测
Keywords
Phase space reconstruction Hermite neural network Particle swarm optimisation (PSO) Chaotic time series prediction
基金项目
作者资料
李瑞国,硕士生,主研领域:智能优化与应用。张宏立,副教授。王雅,硕士生。 。
文章摘要
针对混沌时间序列的混沌性,提出一种改进的相空间重构方法——交集寻优法;针对传统的BP神经网络、RBF神经网络及AR模型对混沌时间序列预测效率和预测精度较低的缺点,提出两种不同的Hermite神经网络预测模型。以四阶蔡氏电路为模型,结合粒子群算法建立预测模型。仿真结果表明,利用交集寻优法进行相空间重构能很好地保留原系统的动力学特性,证实了该方法的有效性;Hermite神经网络较传统的预测模型精度更高,便于基于粒子群算法的Hermite神经网络预测方法的推广和应用。
Abstract
For chaotic property of chaotic time series, we proposed an improved phase space reconstruction method — the intersection optimisation method. In view of the shortcomings of traditional BP neural network, RBF neural network and AR model in low prediction efficiency and accuracy on chaotic time series, we put forward two different Hermite neural network prediction models. Taking the fourth-order chua’s circuit as the model we built the prediction model in combination with PSO algorithm. Simulation results indicated that to reconstruct phase space using intersection optimisation method could well keep the dynamics characteristic of original system, thus the effectiveness of the method was confirmed. Hermite neural network has higher prediction accuracy than traditional neural network, it is easy to promote and apply the PSO-based Hermite neural network prediction method.
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