Multi-Input Single-Output Nonlinear Adaptive Digital Filters Using Recurrent Neural Networks

Jianming LU
Xiaoqiu WANG
Takashi YAHAGI

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E84-A    No.8    pp.1942-1950
Publication Date: 2001/08/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)
Category: Nonlinear Signal Processing
multi-input single-output system,  recurrent neural network,  nonlinear adaptive digital filter,  

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Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent in practical systems, however, nonlinear adaptive filtering will be more desirable. This paper presents a design method for multi-input single-output nonlinear adaptive digital filters using recurrent neural networks. Furthermore, in comparison with this method and the method based on the conventional linear theory, if the proposed method is used, better results can be obtained, and, it is possible that the learning efficiency is improved, because the parallel learning is carried out in this method. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.