Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409102 | Neurocomputing | 2008 | 15 Pages |
Abstract
This study proposes a fuzzy neural network (FNN) that can process both fuzzy inputs and outputs. The continuous genetic algorithm (CGA) is employed to enhance its performance. Both the simulation and real-world problem results show that the proposed CGA-based FNN can obtain the relationship between fuzzy inputs and outputs. CGA can not only shorten the training time but also increase the accuracy for the FNN.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
R.J. Kuo, S.M. Hong, Y. Lin, Y.C. Huang,