Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 21, Issue 2, Pages 209-220Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2012.2208974
Keywords
Spiking neural P systems (SN P systems); weighted fuzzy production rules; weighted fuzzy reasoning; weighted fuzzy spiking neural P systems (WFSN P systems)
Funding
- National Natural Science Foundation of China [61170030, 61174058]
- Sichuan Key Laboratory of High-Performance Scientific Computing [SZJJ2012-002]
- Importance Project Foundation of the Education Department of Sichuan Province [12ZA163]
- Importance Project Foundation of Xihua University, China [Z1122632]
- National Key Basic Research Program, China [2012CB215202]
- 111 Project [B12018]
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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 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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