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Knowledge representation using fuzzy spiking neural P system

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4 Author(s)
Tao Wang ; Sch. of Electr. & Inf. Eng., Univ. of Xihua, Chengdu, China ; Jun Wang ; Hong Peng ; Yanli Deng

This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.

Published in:

Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on

Date of Conference:

23-26 Sept. 2010