Neural fuzzy systems: A tutorial and an application

Mark I. Hwang, Jerry W. Lin

Research output: Contribution to journalArticlepeer-review

Abstract

Fuzzy logic has gained tremendous popularity in recent years as its applications are found in areas ranging from consumer products to industrial process control and portfolio management. Along with neural networks and genetic algorithms, fuzzy logic constitutes three cornerstones of "soft computing." Unlike the traditional or hard computing, soft computing strives to model the pervasive imprecision of the real world. Solutions derived from soil computing are generally more robust, flexible, and economical. In addition, constituent technologies of soft computing are generally complementary rather than competitive. As a result, many hybrid systems have been proposed to integrate these complementary technologies. This study review's fuzzy logic and neural networks and illustrates how they can be integrated to provide a better solution. In an empirical test, the integrated neural fuzzy system significantly outperformed a traditional statistical model in predicting pension accounting adoption choices.

Original languageEnglish
Pages (from-to)27-31
Number of pages5
JournalJournal of Computer Information Systems
Volume40
Issue number4
StatePublished - Jun 2000

Keywords

  • Fuzzy logic
  • Neural networks
  • Pension accounting choices

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