TIN: A TRAINABLE INFERENCE NETWORK.

C. L. Winter; T. W. Ryan; C. J. Turner

TIN: A TRAINABLE INFERENCE NETWORK.

C. L. Winter, T. W. Ryan, C. J. Turner

Research output: Contribution to conference › Paper › peer-review

Abstract

The Trainable Inference Network (TIN) is an adaptive network that can learn the functionality of any finite-state machine. TIN is composed of two modified adaptive resonance circuits (ARCs) that learn transition and output tables and an auxiliary assembly of control nodes that facilitates state transitions. The first ARC learns to recognize current-state, input, next-state patterns appearing on separate slabs; the other learns current-state, input, output patterns. Features of TIN's macrocircuit and dynamics are described, focusing on the first, state-transition ARC. TIN is then taught the states, input, and transitions of two simple finite-state machines. The results are summarized, and future research directions are indicated.

Original language	English (US)
Pages	ii/777-785
State	Published - 1987

ASJC Scopus subject areas

General Engineering

Cite this

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title = "TIN: A TRAINABLE INFERENCE NETWORK.",

abstract = "The Trainable Inference Network (TIN) is an adaptive network that can learn the functionality of any finite-state machine. TIN is composed of two modified adaptive resonance circuits (ARCs) that learn transition and output tables and an auxiliary assembly of control nodes that facilitates state transitions. The first ARC learns to recognize current-state, input, next-state patterns appearing on separate slabs; the other learns current-state, input, output patterns. Features of TIN's macrocircuit and dynamics are described, focusing on the first, state-transition ARC. TIN is then taught the states, input, and transitions of two simple finite-state machines. The results are summarized, and future research directions are indicated.",

author = "Winter, {C. L.} and Ryan, {T. W.} and Turner, {C. J.}",

year = "1987",

language = "English (US)",

pages = "ii/777--785",

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T2 - A TRAINABLE INFERENCE NETWORK.

AU - Winter, C. L.

AU - Ryan, T. W.

AU - Turner, C. J.

PY - 1987

Y1 - 1987

N2 - The Trainable Inference Network (TIN) is an adaptive network that can learn the functionality of any finite-state machine. TIN is composed of two modified adaptive resonance circuits (ARCs) that learn transition and output tables and an auxiliary assembly of control nodes that facilitates state transitions. The first ARC learns to recognize current-state, input, next-state patterns appearing on separate slabs; the other learns current-state, input, output patterns. Features of TIN's macrocircuit and dynamics are described, focusing on the first, state-transition ARC. TIN is then taught the states, input, and transitions of two simple finite-state machines. The results are summarized, and future research directions are indicated.

AB - The Trainable Inference Network (TIN) is an adaptive network that can learn the functionality of any finite-state machine. TIN is composed of two modified adaptive resonance circuits (ARCs) that learn transition and output tables and an auxiliary assembly of control nodes that facilitates state transitions. The first ARC learns to recognize current-state, input, next-state patterns appearing on separate slabs; the other learns current-state, input, output patterns. Features of TIN's macrocircuit and dynamics are described, focusing on the first, state-transition ARC. TIN is then taught the states, input, and transitions of two simple finite-state machines. The results are summarized, and future research directions are indicated.

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UR - http://www.scopus.com/inward/citedby.url?scp=0023602772&partnerID=8YFLogxK

M3 - Paper

SP - ii/777-785

ER -

TIN: A TRAINABLE INFERENCE NETWORK.

Abstract

ASJC Scopus subject areas

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