对复杂混沌时间序列快速预测的前馈神经网络

A neural network for fast prediction of chaotic the series

提出了一种基于前馈神经网络结构的适合于非线性预测的在线学习方法．这种方法吸收了最小二乘法和传统在线Ｂｐ算法的优点，具有收敛速度快、跟踪性能好、适用于非线性预测等特点．应用这种方法．对一些复杂的信号进行了一步预测，虫口模型、Ｈｅｒｏｎ方程和脑电信号的模拟结果验证了新方法的良好特性．

In this paper a real-time non-linear predictive algorithm based on feed-forward neural network was proposed. This method combines the advantages of both least square and Bp algorithm, so it has fast convergent speed and perfect trace performance.Specially, it is suitable for complex non-linear prediction. Some chaotic time series, such as Logistic mapping, Henon equation and EEG signal, were predicted by the proposed algorithm. The simulation results showed the good properties of the method.