ON DESIGNING FUZZY LEARNING NEURAL-AUTOMATA.

L. C. Shiue; R. O. Grondin

ON DESIGNING FUZZY LEARNING NEURAL-AUTOMATA.

L. C. Shiue, R. O. Grondin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A structured approach to the design of neural-like learning automata. A novel fuzzy neuron is used as a basic processing element in building such automata. The possibility of implementing neural networks by VLSI circuits is explored. It is shown that a parallel automaton can be realized directly from specified fuzzy production rules. By reinforcing the fuzzy grades of membership of production rules, a fuzzy learning machine is obtained. This approach can also be applied to the construction of stochastic neural automata by using multiply-add operations instead of max-min rules.

Original language	English (US)
Title of host publication	Unknown Host Publication Title
Editors	Maureen Caudill, Charles T. Butler, San Diego Adaptics
Place of Publication	San Diego, CA, USA
Publisher	SOS Printing
State	Published - 1987

ASJC Scopus subject areas

General Engineering

Cite this

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title = "ON DESIGNING FUZZY LEARNING NEURAL-AUTOMATA.",

abstract = "A structured approach to the design of neural-like learning automata. A novel fuzzy neuron is used as a basic processing element in building such automata. The possibility of implementing neural networks by VLSI circuits is explored. It is shown that a parallel automaton can be realized directly from specified fuzzy production rules. By reinforcing the fuzzy grades of membership of production rules, a fuzzy learning machine is obtained. This approach can also be applied to the construction of stochastic neural automata by using multiply-add operations instead of max-min rules.",

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year = "1987",

language = "English (US)",

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AU - Grondin, R. O.

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N2 - A structured approach to the design of neural-like learning automata. A novel fuzzy neuron is used as a basic processing element in building such automata. The possibility of implementing neural networks by VLSI circuits is explored. It is shown that a parallel automaton can be realized directly from specified fuzzy production rules. By reinforcing the fuzzy grades of membership of production rules, a fuzzy learning machine is obtained. This approach can also be applied to the construction of stochastic neural automata by using multiply-add operations instead of max-min rules.

AB - A structured approach to the design of neural-like learning automata. A novel fuzzy neuron is used as a basic processing element in building such automata. The possibility of implementing neural networks by VLSI circuits is explored. It is shown that a parallel automaton can be realized directly from specified fuzzy production rules. By reinforcing the fuzzy grades of membership of production rules, a fuzzy learning machine is obtained. This approach can also be applied to the construction of stochastic neural automata by using multiply-add operations instead of max-min rules.

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M3 - Conference contribution

AN - SCOPUS:0023561029

BT - Unknown Host Publication Title

A2 - Caudill, Maureen

A2 - Butler, Charles T.

A2 - Adaptics, San Diego

PB - SOS Printing

CY - San Diego, CA, USA

ER -

ON DESIGNING FUZZY LEARNING NEURAL-AUTOMATA.

Abstract

ASJC Scopus subject areas

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