A neuro-fuzzy-genetic classifier for technical applications

Author

Gorzalczany, Marian B. ; Gradzki, Piotr

Author_Institution

Dept. of Electr. & Comput. Eng., Kielce Univ. of Technol., Poland

Volume

1

fYear

2000

fDate

19-22 Jan. 2000

Firstpage

503

Abstract

The paper presents an approach that combines artificial neural networks with fuzzy logic to form a neuro-fuzzy classifier. The proposed system has a feedforward network-like structure that mirrors fuzzy rules. The proposed system is able to learn and to generalize gained knowledge (it comes from the network-like structure) as well as to explain the decisions it makes. Its learning abilities are strengthened by applying a genetic algorithm as a technique of global optimization. The proposed neuro-fuzzy classifier has been successfully applied to the glass identification problem in forensic science.

Keywords

classification; feedforward neural nets; fuzzy logic; fuzzy neural nets; generalisation (artificial intelligence); genetic algorithms; identification; learning (artificial intelligence); artificial neural networks; feedforward network-like structure; forensic science; fuzzy logic; fuzzy rules; genetic algorithm; glass identification problem; global optimization; knowledge generalisation; knowledge learning; learning abilities; neuro-fuzzy-genetic classifier; technical applications; Artificial intelligence; Artificial neural networks; Character generation; Competitive intelligence; Decision support systems; Feedforward systems; Fuzzy logic; Inference algorithms; Intelligent systems; Mirrors;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology 2000. Proceedings of IEEE International Conference on

Print_ISBN

0-7803-5812-0

Type

conf

DOI

10.1109/ICIT.2000.854204

Filename

854204