Application of fuzzy reasoning spiking neural P systems to fault diagnosis

26Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Wang, T., Zhang, G., Rong, H., & Pérez-Jiménez, M. J. (2014). Application of fuzzy reasoning spiking neural P systems to fault diagnosis. International Journal of Computers, Communications and Control, 9(6), 786–799. https://doi.org/10.15837/ijccc.2014.6.1485

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free