...
首页> 外文期刊>IEEE Transactions on Circuits and Systems. 1 >Stable and efficient neural network modeling of discrete-time multichannel signals
【24h】

Stable and efficient neural network modeling of discrete-time multichannel signals

机译:离散多通道信号的稳定高效的神经网络建模

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

This paper presents a neural-network-based recursive modeling scheme that constructs a nonlinear dynamical model for a discrete-time multichannel signal. Using the so-called radial-basis-function (RBF) neural network as a generic nonlinear model structure and the ideas developed in the classical adaptive control theory, we have been able to derive a stable and efficient weight updating algorithm that guarantees the convergence for both the prediction error and the weight error. A griding method based on the spatial Fourier analysis has been modified and applied for setting up the RBF neural, net structure. Simulation analysis is also carried out to highlight the practical considerations in using the scheme.
机译:本文提出了一种基于神经网络的递归建模方案,该方案为离散时间多通道信号构建了非线性动力学模型。使用所谓的径向基函数(RBF)神经网络作为通用非线性模型结构以及经典自适应控制理论中提出的思想,我们已经能够导出一种稳定,有效的权重更新算法,该算法可确保收敛。预测误差和权重误差。改进了基于空间傅立叶分析的网格化方法,并将其应用于建立RBF神经网络结构。还进行了仿真分析,以突出使用该方案的实际考虑。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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