DocumentCode
315342
Title
A neuron-inspired fuzzy relation model of dynamic systems and its learning algorithms
Author
Yingwu, Chen
Author_Institution
Dept. of Syst. Eng. & Math., Nat. Univ. of Defense Technol., Hunan, China
Volume
1
fYear
1997
fDate
1-5 Jul 1997
Firstpage
465
Abstract
In view of fuzzy sets and their operations, three kinds of logic neurons, i.e., AND, OR and AND/OR neurons, are present in this paper. And those neurons can be classified into two types: weighted and relational. Using AND, OR and AND/OR neurons, a fuzzy relational model for dynamic system is provided as well as its learning algorithms. By a simple example, the soundness and the learning capability of the algorithms are verified
Keywords
fuzzy neural nets; learning (artificial intelligence); logic gates; modelling; AND neurons; AND/OR neurons; OR neurons; dynamic systems; fuzzy sets; learning algorithms; logic neurons; neuron-inspired fuzzy relational model; relational neurons; weighted neurons; Biological system modeling; Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Mathematics; Neural networks; Neurons; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location
Barcelona
Print_ISBN
0-7803-3796-4
Type
conf
DOI
10.1109/FUZZY.1997.616412
Filename
616412
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