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On neural networks in identification and control of dynamic systemsThis paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.
Document ID
Document Type
Technical Memorandum (TM)
Phan, Minh (Lockheed Engineering and Sciences Co. Hampton, VA., United States)
Juang, Jer-Nan (NASA Langley Research Center Hampton, VA, United States)
Hyland, David C. (Harris Corp. Melbourne, FL., United States)
Date Acquired
September 6, 2013
Publication Date
June 1, 1993
Subject Category
Report/Patent Number
NAS 1.15:107702
Funding Number(s)
PROJECT: RTOP 585-03-11-09
Distribution Limits
Work of the US Gov. Public Use Permitted.

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NameType 19930021849.pdf STI