Signal recognition by input‐output correlation in associative neural networks

Abstract

AbstractInstead of the conventional static memory model based on an associative memory neural network, this paper proposes a dynamic memory model that is extended to the input‐output system in order to allow interaction with external information. Based on the time correlation between the input signal and the network output, we analyze and investigate the network response to stored, nonstored, and mixed stored pattern signals, and the parameter dependencies inherent in the model. The possibility of a new mechanism, the chaotic dynamic model, which discriminates and identifies pattern signal information by combining the dynamic memory model with its extension is shown to be possible. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 54–64, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10022