Parallel nonlinear adaptive digital filters using recurrent neural networks.

In the field of signal processing, some problems that are difficult to process using linear theory can be solved by nonlinear processing. As for estimation problems when an unknown system is nonlinear, real-time processing is difficult because of the huge amount of calculations required to find the optimum solution when conventional nonlinear process methods are used. On the other hand, research on neural networks that have nonlinear input-output relationships has attracted attention. These have been used in various areas such as pattern recognition and nonlinear system estimation. In this paper, a method of designing parallel recurrent neural digital filters by introducing several small, recurrent neural networks is proposed for nonlinear adaptive digital filters, which have many parameters and need real-time processing. The proposed method gives better results than conventional methods based on linear and nonlinear theory. Furthermore, learning efficiency can be improved by the proposed method because parallel learning is performed. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(12): 91–101, 1997 [ABSTRACT FROM AUTHOR]