Title :
A neuron-inspired fuzzy relation model of dynamic systems and its learning algorithms
Author_Institution :
Dept. of Syst. Eng. & Math., Nat. Univ. of Defense Technol., Hunan, China
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;
Conference_Titel :
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7803-3796-4
DOI :
10.1109/FUZZY.1997.616412
