Wang, JunShi, PengPeng, HongPérez Jiménez, Mario de JesúsWang, Tao2018-10-312018-10-312013Wang, J., Shi, P., Peng, H., Pérez Jiménez, M.d.J. y Wang, T. (2013). Weighted Fuzzy Spiking Neural P Systems. IEEE Transactions on Fuzzy Systems, 21 (2), 209-220.1063-6706https://hdl.handle.net/11441/79732Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological be-havior of biological spiking neurons. In order to make SN P sys-tems 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 ac-complish 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.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Spiking neural P systems (SN P systems)Weighted fuzzy production rulesWeighted fuzzy reasoningWeighted fuzzy spiking neural P systems (WFSN P systems)info:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1109/TFUZZ.2012.2208974