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|Title:||Weighted fuzzy spiking neural P systems|
|Citation:||IEEE Transactions on Fuzzy Systems, 2013; 21(2):209-220|
|Publisher:||IEEE-Inst Electrical Electronics Engineers Inc|
|Jun Wang, Peng Shi, Hong Peng, Mario J. Pérez-Jiménez and Tao Wang|
|Abstract:||Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper called weighted fuzzy spiking neural P systems (WFSNP systems). New elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule, and two types of neurons, are added to the original definition of SN P systems. This allows WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rulebased system. Furthermore, a weighted fuzzy backward reasoning algorithm, based onWFSN P systems, is developed, which can accomplish dynamic fuzzy reasoning of a rule-based system more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph, and Petri nets, to demonstrate the features and advantages of the proposed techniques.|
|Keywords:||Spiking neural P systems (SN P systems); weighted fuzzy production rules; weighted fuzzy reasoning; weighted fuzzy spiking neural P systems (WFSN P systems)|
|Rights:||© 2012 IEEE|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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