On the training of artificial neural networks with radial basis function using optimum-path forest clustering

In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well known k-means, require the number of classes/clusters previous its performance. Although the number of classes is known in supervised applications, the real number of clusters is extremely hard to figure out, since one class may be represented by more than one cluster. Experiments over 9 datasets together with statistical analysis have shown the suitability of OPF clustering for the RBF training step

Support Vector Machine Approach for Non-Technical Losses Identification in Power Distribution Systems

Electricity consumer fraud is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. In this paper,the approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, Support Vector Machine (SVM), are presented. This approach provides a method of data mining, which involves feature extraction from past consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. Some key advantages of SVM in data clustering, among which is the easy way of using them to fit the data of a wide range of features are discussed here. Finally, some major weakness of using SVM in clustering for NTL identification are identified, which leads to motivate for the scope of Optimum-Path Forest, a new model of NTL identification