NDLI: Discrete time neural network synthesis using input and output activation functions

Content Provider	IEEE Xplore Digital Library
Author	Novakovic, B.M.
Copyright Year	1996
Abstract	A new very fast algorithm for synthesis of a new structure of discrete-time neural networks (NN) is proposed. For this purpose the following concepts are employed: (i) combination of input and output activation functions, (ii) input time-varying signal distribution, (iii) time-discrete domain synthesis and (iv) one-step learning iteration approach. The problem of input-output mappings of time-varying vectors is solved. Simulation results based on the synthesis of a new structure of feedforward NN of an universal logical unit are presented. The proposed NN synthesis procedure is useful for applications to identification and control of nonlinear, very fast, dynamical systems. In this sense a feedforward NN for an adaptive nonlinear robot control is designed. Finally, a new algorithm for the direct inverse modeling of input/output nonquadratic systems is discussed.
Starting Page	533
Ending Page	541
Page Count	9
File Size	964271
File Format	PDF
ISSN	10834419
Volume Number	26
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Network synthesis Signal synthesis Control system synthesis Nonlinear control systems Programmable control Adaptive control Robot control Inverse problems
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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Comments on Discrete time neural network synthesis using input and output functions

Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time

Discrete time neural network synthesis using input and output activation functions

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