The reliability of insulation systems is a major requirement of any power apparatus. The incidence of minor flaws and irregularities such as voids, surface imperfections etc, in insulation systems is however inevitable and leads to partial discharges (PD). Classification of PD patterns plays an important role during manufacturing and on-site assessment of power apparatus. The innovative trend o...

Through this paper, some performance increasing methods for probabilistic neural network (PNN) are presented. These methods are tested with the glass benchmark database which has an irregular class distribution. Selection of a good training dataset is one of the most important issue. Therefore, a new data selection procedure was proposed. A data replication method is applied to the rare events ...

In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less mu...

BACKGROUND Artificial neural networks is one of pattern analyzer method which are rapidly applied on a bio-medical field. OBJECTIVE The aim of this research was to propose an appendicitis diagnosis system using artificial neural networks (ANNs). METHODS Data from 801 patients of the university hospital in Dongguk were used to construct artificial neural networks for diagnosing appendicitis ...

A methodology based on multi-weights neural network (MWNN) is presented to identify faults of rolling bearing. With considerations of difficulties in analyzing rolling bearing vibration data, we analyzed how to extract time domain feature parameters of faults. Further, the time domain feature parameters extracted from fault signals are utilized to train multi-weights neural network for achievin...

A multi-probabilistic neural network (multi-PNN) classification structure has been designed for distinguishing onemonth abstinent heroin addicts from normal controls by means of the Event-Related Potentials P600 component, selected at 15 scalp leads, elicited under a Working Memory (WM) test. The multi-PNN structure consisted of 15 optimally designed PNN lead-classifiers feeding an end-stage PN...

The purpose of this article is to demonstrate the application of probabilistic neural networks (PNNs) as a classification tool in the slope stability estimation. PNNs are applied to estimate slope stability according to the slope geometric shapes and soil mechanical parameters. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to pro...

An extension of the well-known probabilistic neural network (PNN) to generalized locally recurrent PNN (GLR PNN) is introduced. The GLR PNN is derived from the original PNN by incorporating a fully connected recurrent layer between the pattern and output layers. This extension renders GLR PNN sensitive to the context in which events occur, and therefore, capable of identifying temporal and spat...

This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multilayered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule bas...

The analysis of pathological voice is a challenging and an important area of research in speech processing. Acoustic voice analysis can be used to characterize the pathological voices with the aid of the speech signals recorded from the patients. This paper presents a method for the identification and classification of pathological voice using Artificial Neural Network. Multilayer Perceptron Ne...