Decompositional Rule Extraction from Artificial Neural Networks and Application in Analysis of Transformers

Abstract

The artificial neural networks represent efficient computational models that are widely used to solve problems of difficult solution in Artificial Intelligence. The greatest difficulty associated with the use of Artificial Neural Networks (ANN) is in obtaining knowledge about its behavior, because of that ANNs are also considered as black-box methods. This paper presents a brief history of methods of extraction of knowledge, and in detail a method of interpreting the behavior of an artificial neural network by establishing a relation of equality between certain classes of neural networks and systems based on fuzzy rules, with modifications that allow the acquisition of rules coherent with the domain of the variables of the problem. An example of application is used to illustrate the method, considering the identification of incipient faults in transformers by using data from gas dissolved in transformer oil.Index Terms--Knowledge rule extraction, neural networks, fuzzy rule-based systems, transformer failure diagnosis.

Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis

“…Percentage concentration of H 2 , C 2 H 4 and C 2 H 2 were used as the three inputs to the ANN and the transformer faults were classified as discharge, partial discharge, and thermal fault [1]. Amora et al in 2009 also extracted decompositional rules from ANN for analysis of transformers [2]. Both of these methods derived fuzzy rules and are architectural analysis based methods.…”