Nonlinear signal processing and system identification: applications to time series from electrochemical reactions

Abstract We show how nonlinear signal processing techniques can be used for extracting simple dynamic models from complex experimental time series. A neural network analysis is applied to measurements of current versus time from an experimental system where the electrodissolution of copper in a phosphoric acid solution takes place. We investigate transitions from steady to oscillatory behavior and from period-one to period-two oscillations. Such procedures can be used in the analysis of systems for which no adequate phenomenological models exist.

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