Weighted Fuzzy Spiking Neural P Systems

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Wang, Jun, Shi, Peng, Peng, Hong, Perez-Jimenez, M. J and Wang, Tao (2013) Weighted Fuzzy Spiking Neural P Systems. IEEE Transactions on Fuzzy Systems, 21 (2). pp. 209-220. ISSN 1063-6706


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 (WFSN P 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 rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm, based on WFSN 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.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/23669
DOI 10.1109/TFUZZ.2012.2208974
Official URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > Division/Research > College of Science and Engineering
Keywords ResPubID26717, spiking neural P systems (SN P systems), weighted fuzzy production rules, weighted fuzzy reasoning, weighted fuzzy spiking neural P systems (WFSN P systems), fuzzy logic, fuzzy neural nets, fuzzy reasoning, knowledge based systems, knowledge representation
Citations in Scopus 152 - View on Scopus
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