Title

Neural fuzzy systems: A tutorial and an application.

SelectedWorks Author Profiles:

Wenshan Lin

Document Type

Article

Publication Date

2000

ISSN

0887-4417

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 "soil computing." Unlike the traditional or hard computing, soil computing strives to model the pervasive imprecision of the real world Solutions derived from soft computing are generally more robust, flexible, and economical. In addition, constituent technologies of soil computing are generally complementary rather than competitive. As a result, many hybrid systems have been proposed to integrate these complementary technologies. This study reviews fuzzy logic and neural networks and illustrates how they can he 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.

Comments

Citation only. Full-text article is available through licensed access provided by the publisher. Members of the USF System may access the full-text of the article through the authenticated link provided.

Publisher

Taylor & Francis

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.