A transformer is an important part of the power system. Existing fault diagnosis methods are still limited by accuracy and efficiency solution excessively rely on manpower. In this paper, a novel neural network designed to overcome issue. Based traditional method judging ratio dissolved gas in internal insulation oil, fast model was built with improved probabilistic (PNN). The particle swarm op...

In most application of the data classifications, the data sets contain both continuous and categorical variables. In other word, multivariate data sets containing mixtures of continuous and categorical variables arise frequently in practice. This paper presents a novel Probability Neural Network (PNN) which can classify the data for both continuous and categorical input data types. The case wit...

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is pro...

This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models ...

Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support vector machine (SVM) and probabilistic neural network (PNN). A number of financial ratios are used as explanatory variables. Test performanc...

We consider the probabilistic neural network (PNN) that is a mixture of Gaussian basis functions having different variances. Such a Gaussian heteroscedastic PNN is more economic, in terms of the number of kernel functions required, than the Gaussian mixture PNN of a common variance. The expectation-maximisation (EM) algorithm, although a powerful technique for constructing maximum likelihood (M...

This paper proposes a system capable of identifying and categorising web pages, on the basis of information filtering. The system is a three layer Probabilistic Neural Network (PNN) with biases and radial basis neurons in the middle layer and competitive neurons in the output layer. The domain of study involves the e-commerce area. Thus, the PNN scopes to identify e-commerce web pages and class...

This paper address the detection of dust storms based on a probabilistic analysis of multispectral images. We develop a feature set based on the analysis of spectral bands reported in the literature. These studies have focused on the visual identification of the image channels that reflect the presence of dust storms through correlation with meteorological reports. Using this feature set we dev...

Agile Software development has become famous in industries and replacing the traditional methods of software development. A correct estimation of effort in this concept still remains an argument in industries. Thus, the industry must be able to estimate the effort necessary for software development using agile methodology. For estimating effort different types of neural-networks Probabilistic N...

The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation. EXECUTIVE SUMMARY A significant body of research on advanced techniques for automa...