Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
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DOI：
10.15837/ijccc.2014.6.1485
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Tao Wang;Gexiang Zhang;Haina Rong;M. Pérez-Jiménez
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文献类型：
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作者：
Tao Wang;Gexiang Zhang;Haina Rong;M. Pérez-Jiménez
本文讨论了梯形模糊数模糊推理尖峰神经P系统（tFRSN P系统）在电力系统故障诊断中的应用，利用基于神经元动态放电机制的矩阵模糊推理算法，将tFRSN P系统的推理能力从经典推理发展为模糊推理。实例分析表明了该方法的有效性。本文还从知识表示和推理过程的角度对所提出的方法与几种主要的故障诊断方法进行了比较。
This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process.