ART 2 - an unsupervised neural network for PD pattern recognition and classification

Author(s): Gopal, S. | Karthikeyan, B. | Venkatesh, S. |

Year: 2006

Citation: EXPERT SYSTEMS WITH APPLICATIONS Volume: 31 Issue: 2 Pages: 345-350

Abstract: This paper introduces a method of classifying partial discharges of unknown origin. The innovative trend of using Artificial Neural Network (ANN) towards classification of Partial Discharge (PD) patterns is cogent and discernible. The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition is explained here. To ensure the suitability and reliability of chosen network for PD pattern recognition, the network is tested with the well known Iris plant database and alphabet character for recognition & classification. Further more the network is trained with various combinations of (phi-q-n distributions of PD patterns and tested. It is shown that the ART 2 network is able to classify the PD patterns. The paper ends with analyzing the efficacy of multifarious features selected in the measurement space. Also the validation of input features is done using Hold-One-Out method and partial set training technique.

Topics: Machine Learning, Applications: Industrial Control, Models: ART 2 / Fuzzy ART,

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