System identification with dynamic neural networks

Prasanta K. De, Massoud Amin, Ervin Y. Rodin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The application of dynamic neurons to system identification is investigated. Similar networks can easily be extended to the control of systems in an open- or closed-loop fashion. States of the dynamic neurons evolve as x = -ax+bu, where u is the input to a neuron. The output of the dynamic neurons are given by y = λ(x), where λ(.) is a nonlinear function. The Distributed Dynamic Back Propagation (DDBP) technique is used to back propagate the hierarchical-network output errors. This implementation is useful because it yields a common platform on which many identification and control problems can be investigated.

Original languageEnglish (US)
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, Y.C. Shin
PublisherASME
Pages97-102
Number of pages6
Volume2
StatePublished - Dec 1 1992
Externally publishedYes
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: Nov 15 1992Nov 18 1992

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA
Period11/15/9211/18/92

Fingerprint Dive into the research topics of 'System identification with dynamic neural networks'. Together they form a unique fingerprint.

Cite this