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

J. L. Hudson, M. Kube, R. A. Adomaitis, I. G. Kevrekidis, A. S. Lapedes, R. M. Farber

Research output: Contribution to journalArticle

31 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2075-2081
Number of pages7
JournalChemical Engineering Science
Volume45
Issue number8
DOIs
StatePublished - 1990

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Keywords

  • Bifurcations
  • Electrochemical Oscillations
  • Neural Networks
  • Nonlinear Dynamics
  • Signal Processing

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    Hudson, J. L., Kube, M., Adomaitis, R. A., Kevrekidis, I. G., Lapedes, A. S., & Farber, R. M. (1990). Nonlinear signal processing and system identification: applications to time series from electrochemical reactions. Chemical Engineering Science, 45(8), 2075-2081. https://doi.org/10.1016/0009-2509(90)80079-T