一种预测混沌时间序列的模糊神经网络方法

给出了一种预测混沌时间序列的模糊神经网络及其学习方法，给出的方法能直接从数据中提取模糊规则，经过优化得到最佳模糊规则库，并利用神经网络的自学习功能修改隶属函数的参数和网络的权值，减少了规则的匹配过程，加快了推理速度，增强了网络的自适应能力. 使用该神经网络及其学习方法对Lorenz混沌时间序列进行了预测仿真研究，试验结果表明给出的预测工具和方法是有效的. 关键词： 模糊神经网络 模糊规则提取 混沌时间序列预测

A neuro-fuzzy method for predicting the chaotic time series

A neuro-fuzzy approach based on a novel hybrid learning method is presented, which can generate the best fuzzy rule set automatically from the desired input-output data pairs only and can give the initial neuro-fuzzy system and the initial parameters of fuzzy membership functions. Then the parameters of fuzzy membership functions and the weights can be easily tuned by employing neural network's self-learning techniques. This approach reduces the rule matching time and accelerates the speed of the fuzzy logic referencing and improves the adaptability of the neuro-fuzzy system. Using the proposed neuro-fuzzy system and the learning algorithms we simulated the prediction of the Lorenz chaotic time series, the results demonstrate the effectiveness of the chaotic time series prediction approach.