@article {882,
	title = {Weighted Fuzzy Spiking Neural P Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {2},
	year = {2013},
	month = {07/2013},
	pages = {209-220},
	publisher = {IEEE Computational Intelligence Society},
	edition = {21},
	abstract = {Spiking neural P systems (SN P systems) are a new class of computing models inspired by neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable to represent and process 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). Some 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 original definition of SN P systems, which allow 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 systems 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 or advantages of the proposed techniques.},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2012.2208974},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6242397},
	author = {Jun Wang and Peng Shi and Hong Peng and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Wang}
}
