Fuzzy reasoning spiking neural P system for fault diagnosis

Authors:
Hong Peng;Jun Wang;Mario J. PéRez-JiméNez;Hao Wang;Jie Shao;Tao Wang
Affiliations:
School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China and Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, Univers ...;School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China;Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain;School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China;School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China;School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China
Venue:
Information Sciences: an International Journal
Year:
2013

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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'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.