中文摘要：

为了利用径向基函数（RBF）神经网络对混沌序列进行精确和快速的在线预测,提出一种在线构造变结构RBF神经网络的序贯学习算法.该算法建立实时更新的滑动数据窗口,通过学习窗口内的数据对隐节点进行增加和删除,动态确定RBF神经网络隐节点的数目及中心位置,并对隐层至输出层的连接权值进行在线调整.该算法具有调节参数少、学习速度快以及所得网络结构精简等特点.将该网络用于Mackey-Glass混沌时间序列的在线预测实验,结果验证该算法对该混沌序列具有良好的在线动态辨识和预测性能.

英文摘要：

To improve the accuracy and the speed of on-line chaotic time series prediction via radial basis function（RBF） network,a sequential learning algorithm is presented for on-line constructing variable structure RBF network.A sliding window is constructed.By learning real-time updated data in the window,the parameters of the connecting weights,number of hidden units and center locations are dynamically tuned.The algorithm achieves parsimonious RBF network quickly,while only a small number of tuning parameters are employed.The variable structure network is applied to Mackey-Glass chaotic time series on-line prediction.The results demonstrate that network possesses satisfactory on-line dynamic identification and prediction performance.