Computation scheme for the general purpose VLSI fuzzy inference engine as expert system

Yoshiyasu Takefuji, Meng Hiot Lim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (σ) and the method to compute σ to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.

Original languageEnglish
Pages (from-to)109-116
Number of pages8
JournalKnowledge-Based Systems
Volume2
Issue number2
DOIs
Publication statusPublished - 1989
Externally publishedYes

Fingerprint

Inference engines
Fuzzy inference
Expert systems
Fuzzy sets
Hardware
Fuzzy set theory
VLSI circuits
Membership functions
Decision making
Expert system
Inference

Keywords

  • fuzzy computation
  • fuzzy expert systems
  • fuzzy inference engines

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Computation scheme for the general purpose VLSI fuzzy inference engine as expert system. / Takefuji, Yoshiyasu; Lim, Meng Hiot.

In: Knowledge-Based Systems, Vol. 2, No. 2, 1989, p. 109-116.

Research output: Contribution to journalArticle

@article{cc223ee7c70c475f9f74dd47748a7c27,
title = "Computation scheme for the general purpose VLSI fuzzy inference engine as expert system",
abstract = "Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (σ) and the method to compute σ to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.",
keywords = "fuzzy computation, fuzzy expert systems, fuzzy inference engines",
author = "Yoshiyasu Takefuji and Lim, {Meng Hiot}",
year = "1989",
doi = "10.1016/0950-7051(89)90014-2",
language = "English",
volume = "2",
pages = "109--116",
journal = "Knowledge-Based Systems",
issn = "0950-7051",
publisher = "Elsevier",
number = "2",

}

TY - JOUR

T1 - Computation scheme for the general purpose VLSI fuzzy inference engine as expert system

AU - Takefuji, Yoshiyasu

AU - Lim, Meng Hiot

PY - 1989

Y1 - 1989

N2 - Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (σ) and the method to compute σ to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.

AB - Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (σ) and the method to compute σ to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.

KW - fuzzy computation

KW - fuzzy expert systems

KW - fuzzy inference engines

UR - http://www.scopus.com/inward/record.url?scp=45149146234&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=45149146234&partnerID=8YFLogxK

U2 - 10.1016/0950-7051(89)90014-2

DO - 10.1016/0950-7051(89)90014-2

M3 - Article

VL - 2

SP - 109

EP - 116

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

IS - 2

ER -