Implementation of Fuzzy Inference Systems Using Neural Network Techniques

Abstract

Fuzzy inference systems work well in many control applications. One drawback, however, is determining membership functions and inference control rules required to implement the system, which are usually supplied by 'experts'. One alternative is to use a neural network-type architecture to implement the fuzzy inference system, and neural network-type training techniques to 'learn' the control parameters needed by the fuzzy inference system. By using a generalized version of a neural network, the rules of the fuzzy inference system can be learned without the assistance of experts.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1992
Accession Number
ADA252929

Entities

People

  • Billy E. Hudgins Jr.

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Adaptive Training
  • Algorithms
  • Batch Processing
  • Cardiovascular Physiological Phenomena
  • Classification
  • Control Systems
  • Data Sets
  • Department Of Defense
  • Electrical Engineering
  • Engineering
  • Equations
  • Fuzzy Logic
  • Neural Networks
  • Security
  • Simulations
  • Training
  • United States Government

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks