基于模糊边界模块化神经网络的混沌时间序列预测

提出一种模糊边界模块化神经网络（FBMNN）的混沌时间序列预测方法，该方法先对混沌时间序列观测点重构的相空间进行模块化划分，划分点的选取由遗传算法自动寻优.然后定义一个模糊隶属度函数，在划分边界一侧按照一定的模糊隶属度设定模糊边界带，通过模糊化处理，解决了各模块划分点附近预测结果的跳跃问题.最后每一模块，及其模糊边界的样本点都对应一个递归神经网络进行训练，通过预测合成模块输出结果.该方法对三个混沌时间序列基准数据集Mackey-Glass，Lorenz，Henon进行实验，结果表明该方法有效地提高了混沌时间序列预测效果.关键词：模糊边界模块化神经网络混沌时间序列预测

Chaotic time series prediction based on fuzzy boundary modular neural networks

A fuzzy boundary modular neural network （FBMNN） is proposed for the chaotic time series prediction. First，the reconstructed phase space is divided into several subspaces and the divided points are evaluated by genetic algorithms. Then a fuzzy membership function is defined and the fuzzy boundary is set on the border according to the fuzzy membership. Through this fuzzy treatment，the jumping problem of the predicted data near the divided points are solved. Finally the data points of each module and its fuzzy boundary are input to a recurrent neural network for training and the output predicted points are synthesized by a synthesis forecast module. The effectiveness of FBMNN is evaluated by using three benchmark chaotic time series data sets：the Mackey-Glass series，Lorenz series，and Henon series. The simulation results show that FBMNN improves the performance of chaotic time series prediction.