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Identification and control of dynamical systems using neural networks.
MedLine Citation:
PMID:  18282820     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described.
Authors:
K S Narendra; K Parthasarathy
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  1     ISSN:  1045-9227     ISO Abbreviation:  IEEE Trans Neural Netw     Publication Date:  1990  
Date Detail:
Created Date:  2008-02-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4-27     Citation Subset:  -    
Affiliation:
Dept. of Electr. Eng., Yale Univ., New Haven, CT.
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