Weighted Fuzzy Spiking Neural P Systems

• Published in: IEEE transactions on fuzzy systems Vol. 21; no. 2; pp. 209 - 220
• Main Authors: Wang, Jun, Shi, Peng, Peng, Hong, Perez-Jimenez, Mario J., Wang, Tao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Educational institutions; Fuzzy logic; Fuzzy reasoning; Knowledge based systems; Neurons; Production; Spiking neural P systems (SN P systems); Studies; Tin; weighted fuzzy production rules; weighted fuzzy reasoning; weighted fuzzy spiking neural P systems (WFSN P systems)
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2012.2208974

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.