Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Fuzzy logic and neural network handbook
Fuzzy logic and neural network handbook
A Hypergraph Based Clustering Algorithm for Spatial Data Sets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Determination of neural-network topology for partial discharge pulse pattern recognition
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Partial discharge (PD) is amajor cause of failure of power apparatus and hence itsmeasurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination ofmultisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.