Electronic Resource
Springer
Neural processing letters
4 (1996), S. 75-81
ISSN:
1573-773X
Keywords:
fuzzy systems
;
neural networks
;
neuro-fuzzy modeling
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy models is proposed, which reduces the number of rules by means of a systematic procedure that consists in successively removing a rule and updating the remaining rules in such a way that the overall input-output behavior is kept approximately unchanged over the entire training set. A formulation of the proper update is described and a criterion for choosing the rules to be removed is also provided. Initial experimental results show the effectiveness of the proposed method in reducing the complexity of a neuro-fuzzy system by using its input-output data.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00420616
Permalink
| Library |
Location |
Call Number |
Volume/Issue/Year |
Availability |
