## Periodic Signals in Neural-Like Networks - an Averaging Analysis

• The paper describes the concepts and background theory of the analysis of a neural-like network for the learning and replication of periodic signals containing a finite number of distinct frequency components. The approach is based on a two stage process consisting of a learning phase when the network is driven by the required signal followed by a replication phase where the network operates in an autonomous feedback mode whilst continuing to generate the required signal to a desired accuracy for a specified time. The analysis focusses on stability properties of a model reference adaptive control based learning scheme via the averaging method. The averaging analysis provides fast adaptive algorithms with proven convergence properties.

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Author: R. Reinke, D. Prätzel-Wolters urn:nbn:de:hbz:386-kluedo-5566 Berichte der Arbeitsgruppe Technomathematik (AGTM Report) (154) Preprint English 1995 1995 Technische Universität Kaiserslautern 2000/06/07 Fachbereich Mathematik 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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