基于模糊模型的混沌时间序列预测

对于复杂、病态、非线性动态系统，基于模糊集合的模糊模型，利用模糊推理规则描述动态系统的特性，是一种有效方法.讨论了利用模糊建模方法实现非线性系统的建模和预测.首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数.采用该方法对Mackey Glass混沌时间序列进行预测试验，结果表明利用本方法可以在线或者离线能对Mackey Glass混沌时间序列进行准确预测，证明了本方法的有效性. 关键词： 模糊竞争学习 混沌时间序列 卡尔曼滤波

Prediction of chaotic time series based on fuzzy model

For dynamic systems with complex, ill-conditioned, or nonlinear characteristics, the fuzzy model based on fuzzy sets is very useful to describe the properties of the dynamic systems using fuzzy inference rules. Modeling and prediction of nonlinear systems using fuzzy modeling is discussed in this paper. First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm. To illustrate the performance of the proposed method, simulations on the chaotic Mackey-Glass time series prediction are performed. Combining either off-line or on-line learning with the proposed method, we can show that the chaotic Mackey-Glass time series are accurately predicted, and demonstrate the effectiveness.