Fuzzy reasoning spiking neural P system for fault diagnosis


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 systemswhen applying them to the fault diagnosis domain. Thus, we extend SNP systems by introducing somenew ingredients (such as three types of neurons, fuzzy logic and newfiring mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable tomodel fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithmbased on FRSN P systems is developed according to neuron’s dynamic firingmechanism. 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. 2012 Elsevier Inc. All rights reserved.

DOI: 10.1016/j.ins.2012.07.015

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@article{Peng2013FuzzyRS, title={Fuzzy reasoning spiking neural P system for fault diagnosis}, author={Hong Peng and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Hao Wang and Jie Shao and Tao Wang}, journal={Inf. Sci.}, year={2013}, volume={235}, pages={106-116} }