Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

Wang, Tao; Zhang, Gexiang; Rong, Haina; Pérez–Jiménez, Mario J.

doi:10.15837/ijccc.2014.6.1485

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…Since the introduction of the first FRSNPS model in [20], different variants of FRSNPS models have been introduced where the value of the spikes inside the neurons is represented by a triangular fuzzy number (TFRSNPS) [27], intuitionistic fuzzy numbers (IFSNPS) [70], interval-valued fuzzy numbers (IVFSNPS) [71,72], real numbers (rFRSNPS) [21] and trapezoidal fuzzy numbers (tFRSNPS) [73]. Additionally, a temporal fuzzy reasoning spiking neural P systems with real numbers (rTFRSNPS) was proposed [74] The IFSNPS models are also very effective in the identification of faults in power systems where the messages received from the SCADA systems are incomplete and uncertain.…”

Section: Power Systems Fault Diagnosismentioning

confidence: 99%

On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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Section: Power Systems Fault Diagnosismentioning

confidence: 99%

On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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“…From 1 Π and Figure 4, the input neurons (16,15), (16,16), (16,17), (17,14), (18,13), (19,12), (20,13), (20,14), (21,15), (21,16), (21,17), (22,12), (22,15), (23,16), (24,13), (25,14), (26, 17)}; Otherwise, 0…”

Section: Frsn P Systems Diagnosis Matrix Reasoning Stepsmentioning

confidence: 99%

“…( ) 32 17 (2,17), (3,18), (4,19), (5,20), (6,21), (7,22), (8,23), (9,24), (10,25), (11,26), (12,27), (13,28), (14,29), (15,30), (16,31), (17, 32)}; Otherwise, 0…”

Section: Frsn P Systems Diagnosis Matrix Reasoning Stepsmentioning

confidence: 99%

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Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

Yahya¹,

Ai²,

Yahya³

2016

JILSA

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This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with "low", "medium", and "high" descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

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“…It is likely that something happens unexpectedly in the systems and causes serious problems due to a variety of reasons, such as unfavorable weather, bad environment or a long time of working. As a result, making full use of sensor reports information is extremely significant to make a reasonable decision in fault diagnosis [58,81].…”

Section: Introductionmentioning

confidence: 99%

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

Dong

Zhang

et al. 2019

INT J COMPUT COMMUN

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Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

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Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

Cited by 20 publications

References 22 publications

On Applications of Spiking Neural P Systems

On Applications of Spiking Neural P Systems

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

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