@article {879,
	title = {Fuzzy reasoning spiking neural P system for fault diagnosis},
	journal = {Information Sciences},
	volume = {235},
	year = {2013},
	month = {06/2013},
	pages = {106{\textendash}116},
	publisher = {Elsevier},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron{\textquoteright}s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.},
	keywords = {Fault diagnosis; P systems; Spiking neural P systems; Fuzzy knowledge representation; Fuzzy reasoning},
	issn = {0020-0255},
	doi = {http://dx.doi.org/10.1016/j.ins.2012.07.015},
	url = {(http://dx.doi.org/10.1016/j.ins.2012.07.015)},
	author = {Hong Peng and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Hao Wang and Jie Shao and Tao Wang}
}
