Particle Swarm Trained Neural Network for Fault Diagnosis of Transformers by Acoustic Emission

Kuo, Cheng‐Chien

doi:10.1007/978-3-540-74205-0_103

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Noise-free Recognition Results1

Setup Of Artificial Neural Network1

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2009

2016

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Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Three different methods that include BP, PSO and the proposed PSO-BP are all applied to the same PD recognition problem. The results of the recognition rates are listed out in Table 4 and their comparison (Kuo, 2007) is made in Fig. 13.…”

Section: Noise-free Recognition Resultsmentioning

confidence: 99%

Artificial recognition system for defective types of transformers by acoustic emission

Kuo¹

2009

Expert Systems with Applications

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Section: Noise-free Recognition Resultsmentioning

confidence: 99%

Artificial recognition system for defective types of transformers by acoustic emission

Kuo¹

2009

Expert Systems with Applications

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“…In this paper, multi-layer neural network is used for identification on MATLAB platform with high-speed mathematical operating capability for numerous data (Candela, Mirelli, & Schifani, 2000;Haykin, 1999;Karthikeyan, Gopal, & Venkatesh, 2006Kuo, 2007;Salama & Bartnikas, 2002). The neural network architecture used here consists three layers (input, hidden and output layer), while the learning rule is based on the proposed PSO-BP algorithm and the neuron of input and output layer are decided by the users as shown in Fig.…”

Section: Setup Of Artificial Neural Networkmentioning

confidence: 99%