DocumentCode
2273613
Title
Neuro-fuzzy control using self-organizing neural nets
Author
Zia, Farrukh ; Isik, Can
Author_Institution
Dept. of Electr. & Comput. Eng., Syracuse Univ., NY, USA
fYear
1994
fDate
26-29 Jun 1994
Firstpage
70
Abstract
This paper discusses a new approach to design a fuzzy logic control system, based on the self-organizing map (SOM) neural network. SOM is used to generate multivariate fuzzy state space from system´s input-output data through unsupervised training. The trained SOM is then used as a part of an inference mechanism for a fuzzy logic controller. The proposed method is compared with other fuzzy neural network approaches. Sample data from a chemical plant is used to demonstrate the technique
Keywords
fuzzy control; fuzzy neural nets; inference mechanisms; self-organising feature maps; unsupervised learning; fuzzy logic control; fuzzy neural network; inference mechanism; input-output data; multivariate fuzzy state space; neuro-fuzzy control; self-organizing neural nets; unsupervised training; Clustering algorithms; Control systems; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Humans; Inference algorithms; Neural networks; Organizing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1896-X
Type
conf
DOI
10.1109/FUZZY.1994.343715
Filename
343715
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