Layered URC fuzzy systems: A novel link between fuzzy systems and neural networks

Jeffrey J. Weinschenk; Robert J. Marks; William E. Combs

Layered URC fuzzy systems: A novel link between fuzzy systems and neural networks

Jeffrey J. Weinschenk, Robert J. Marks, William E. Combs

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

Abstract

We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome "corrective" terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.

Original language	English (US)
Pages	2995-3000
Number of pages	6
State	Published - 2003
Event	International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: Jul 20 2003 → Jul 24 2003

Conference

Conference	International Joint Conference on Neural Networks 2003
Country/Territory	United States
City	Portland, OR
Period	7/20/03 → 7/24/03

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

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title = "Layered URC fuzzy systems: A novel link between fuzzy systems and neural networks",

abstract = "We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome {"}corrective{"} terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.",

author = "Weinschenk, \{Jeffrey J.\} and Marks, \{Robert J.\} and Combs, \{William E.\}",

year = "2003",

language = "English (US)",

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note = "International Joint Conference on Neural Networks 2003 ; Conference date: 20-07-2003 Through 24-07-2003",

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T1 - Layered URC fuzzy systems

T2 - International Joint Conference on Neural Networks 2003

AU - Weinschenk, Jeffrey J.

AU - Marks, Robert J.

AU - Combs, William E.

PY - 2003

Y1 - 2003

N2 - We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome "corrective" terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.

AB - We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome "corrective" terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.

UR - https://www.scopus.com/pages/publications/0141703594

UR - https://www.scopus.com/inward/citedby.url?scp=0141703594&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0141703594

SP - 2995

EP - 3000

Y2 - 20 July 2003 through 24 July 2003

ER -

Layered URC fuzzy systems: A novel link between fuzzy systems and neural networks

Abstract

Conference

All Science Journal Classification (ASJC) codes

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