Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

Authors

  • Tao Wang School of Electrical Engineering, Southwest Jiaotong University Chengdu, 610031, China
  • Gexiang Zhang School of Electrical Engineering, Southwest Jiaotong University Chengdu, 610031, China
  • Haina Rong School of Electrical Engineering, Southwest Jiaotong University Chengdu, 610031, China
  • Mario J. Pérez-Jiménez Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla, Sevilla, 41012, Spain

Keywords:

fuzzy reasoning spiking neural P system with trapezoidal fuzzy number, fuzzy reasoning, fault diagnosis, trapezoidal fuzzy number, linguistic term

Abstract

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.

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Published

2014-12-01

Issue

Vol. 9 No. 6 (2014): International Journal of Computers Communications & Control (December)

Section

Articles

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