Porosity is one of the key parameters associated with oil reservoirs. Determination of this petrophysical parameter is an essential step in reservoir characterization. Among different linear and nonlinear prediction tools such as multi-regression and polynomial curve fitting, artificial neural network has gained the attention of researchers over the past years. In the present study, two-dimensi...

A new ensemble algorithm based on K-means clustering and probabilistic neural network called K-meansPNN for classifying the industrial system faults is presented. The proposed technique consists of a preprocessing unit based on K-means clustering and probabilistic neural network. Given a set of data points, firstly the K-means algorithm is used to obtain K-temporary clusters, and then PNN is us...

Probabilistic Neural Network for Predicting the Stability numbers of Breakwater Armor Blocks Doo Kie Kim1, Dong Hyawn Kim2, Seong Kyu Chang1 and Sang Kil Chang1 Summary The stability numbers determining the Armor units are very important to design breakwaters, because armor units are designed for defending breakwaters from repeated wave loads. This study presents a probabilistic neural network ...

* This work was partially supported by the Italian MURST. AbstractThe aim of this paper is to present a novel technique for defect identification by neural networks based on the classification of remote field effect eddy current (RFEC) data. We consider a kind of neural network that does not require a long training and is particularly well suited for fast classification, the Probabilistic Neura...

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on th...

There are some deficiencies in the improved three-ratio method even though it has been widely used in power transformer fault diagnosis. Using artificial neural networks, the power transformer fault diagnosis is improved in this article. With Matlab programming, three different kinds of neural networks, which are Radial Basis Function (RBF) neural network, Learning Vector Quantization (LVQ) neu...

A Probabilistic Neural Network (PNN) is defined as an implementation of statistical algorithm called Kernel discriminate analysis in which the operations are organized into multilayered feed forward network with four layers: input layer, pattern layer, summation layer and output layer. A PNN is predominantly a classifier since it can map any input pattern to a number of classifications. Among t...

Probabilistic Neural Network has received considerable attention nowadays and obtained many successful application. This type of neural system has shown marvelous higher recognition capability compare with that of Back-Propagation neural system. However, this neural has shown some drawbacks, especially on determining the value of its smoothing parameter and its neural structure optimization whe...

statistical process control (spc) charts play a major role in quality control systems, and their correct interpretation leads to discovering probable irregularities and errors of the production system. in this regard, various artificial neural networks have been developed to identify mainly singular patterns of spc charts, while having drawbacks in handling multiple concurrent patterns. in this...

Introduction: Since human health is the issue of Medical Research, correct prediction of results is of a high importance. This study applies probabilistic neural network (PNN) for predicting coronary artery disease (CAD), because the PNN is stronger than other methods. Methods: In this descriptive-analytic study, The PNN method was implemented on 150 patients admitted to the Mazandaran Heart...