• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文OA文献 >A modified quantized kernel least mean square algorithm for prediction of chaotic time series
【2h】

A modified quantized kernel least mean square algorithm for prediction of chaotic time series

机译：改进的量化核最小均方算法用于混沌时间序列预测

页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A modified quantized kernel least mean square (M-QKLMS) algorithm is proposed in this paper, which is an improvement of quantized kernel least mean square (QKLMS) and the gradient descent method is used to update the coefficient of filter. Unlike the QKLMS method which only considers the prediction error, the M-QKLMS method uses both the new training data and the prediction error for coefficient adjustment of the closest center in the dictionary. Therefore, the proposed method completely utilizes the knowledge hidden in the new training data, and achieves a better accuracy. In addition, the energy conservation relation and a sufficient condition for mean-square convergence of the proposed method are obtained. Simulations on prediction of chaotic time series show that the M-QKLMS method outperforms the QKLMS method in terms of steady-state mean square errors.
机译：提出了一种改进的量化核最小均方（M-QKLMS）算法，它是对量化核最小均方（QKLMS）的一种改进，并采用梯度下降法更新了滤波器的系数。与仅考虑预测误差的QKLMS方法不同，M-QKLMS方法将新的训练数据和预测误差都用于字典中最接近中心的系数调整。因此，所提出的方法完全利用了新训练数据中隐藏的知识，并获得了更好的准确性。另外，获得了该方法的能量守恒关系和均方收敛的充分条件。对混沌时间序列的预测仿真表明，就稳态均方误差而言，M-QKLMS方法优于QKLMS方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号