Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

A Study on the Application of Learning Vector Quantization Neural Network in Pattern Classification

2014 ◽  
Vol 525 ◽  
pp. 657-660 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Vector Quantization ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Learning Vector Quantization ◽  
Classification Method ◽  
Two Dimensional ◽  
The One

Standard back propagation (BP) neural network has disadvantages such as slow convergence speed, local minimum and difficulty in definition of network structure. In this paper, an learning vector quantization (LVQ) neural network classifier is established, then it is applied in pattern classification of two-dimensional vectors on a plane. To test its classification ability, the classification results of LVQ neural network and BP neural network are compared with each other. The simulation result shows that compared with classification method based on BP neural network, the one based on LVQ neural network has a shorter learning time. Besides, its requirements for learning samples and the number of competing layers are also lower. Therefore it is an effective classification method which is powerful in classification of two-dimensional vectors on a plane.

N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Valerii Dmitrienko ◽  
Sergey Leonov ◽  
Mykola Mezentsev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Other ◽  
Proximity Function ◽  
Input Information ◽  
Proximity Measures ◽  
The One ◽  
True Values

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other).  For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

PROBABILISTIC NEURAL NETWORK AND ITS ADAPTIVE VERSION — A STOCHASTIC APPROACH TO PD PATTERN CLASSIFICATION TASK

2005 ◽  
Vol 02 (04) ◽  
pp. 333-344 ◽  
Author(s):  
B. KARTHIKEYAN ◽  
S. GOPAL ◽  
S. VENKATESH
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Stochastic Approach ◽  
Diagnostic Techniques ◽  
Single Type ◽  
Destructive Testing ◽  
Theoretical Concepts

The quality of electrical insulation of any power apparatus is an indispensable requirement for its successful and reliable operation. Partial Discharge (PD) phenomenon serves as an effective Non Destructive Testing (NDT) technique and provides an index on the quality of the insulation. The innovative trend of using Artificial Neural Network (ANN) towards the classification of PD patterns is cogent and discernible. In this paper a novel method for the classification of the PD patterns using the original Probabilistic Neural Network (PNN) as well as its variation is elucidated. A preprocessing scheme that extracts pertinent features of PD from the raw data towards achieving a compact ANN has been employed. The classification of single-type insulation defects such as voids, surface discharges and corona has been taken up. The first part of the paper gives a brief on PD, various diagnostic techniques and interpretation. The second part deals with the theoretical concepts of PNN and its adaptive version. The last part provides details on various results and comparisons of the PNN and its adaptive version in PD pattern classification.

scholarly journals Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

2010 ◽  
Vol 61 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Perumal Chandrasekar ◽  
Vijayarajan Kamaraj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Voltage Sag ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

Selecting the architecture of a class of back-propagation neural networks used as approximators

1997 ◽  
Vol 11 (1) ◽  
pp. 33-44 ◽  
Author(s):  
William C. Carpenter ◽  
Margery E. Hoffman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Back Propagation ◽  
Region Of Interest ◽  
Input Output ◽  
Advantages And Disadvantages ◽  
Back Propagation Neural Networks ◽  
Input Layer ◽  
Hidden Layer

AbstractThis paper examines the architecture of back-propagation neural networks used as approximators by addressing the interrelationship between the number of training pairs and the number of input, output, and hidden layer nodes required for a good approximation. It concentrates on nets with an input layer, one hidden layer, and one output layer. It shows that many of the currently proposed schemes for selecting network architecture for such nets are deficient. It demonstrates in numerous examples that overdetermined neural networks tend to give good approximations over a region of interest, while underdetermined networks give approximations which can satisfy the training pairs but may give poor approximations over that region of interest. A scheme is presented that adjusts the number of hidden layer nodes in a neural network so as to give an overdetermined approximation. The advantages and disadvantages of using multiple output nodes are discussed. Guidelines for selecting the number of output nodes are presented.

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

2005 ◽  
Vol 05 (04) ◽  
pp. 411-423 ◽  
Author(s):  
MAJURA F. SELEKWA ◽  
VALERIAN KWIGIZILE ◽  
RENATUS N. MUSSA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Tree Methods ◽  
Decision Tree Methods

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

A STUDY OF ULTRASONIC LIVER IMAGES CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS BASED ON FRACTAL GEOMETRY AND MULTIRESOLUTION ANALYSIS

2004 ◽  
Vol 16 (02) ◽  
pp. 59-67 ◽  
Author(s):  
WEN-LI LEE ◽  
KAI-SHENG HSIEH ◽  
YUNG-CHANG CHEN ◽  
YING-CHENG CHEN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fractal Geometry ◽  
Feature Vector ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Artificial Neural

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers and three various artificial neural networks are included: Bayes classifier, k-nearest neighbor classifier, Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These five different classifiers were investigated to determine their ability to classify various categories of ultrasonic liver images. The investigation was performed on the basis of the same feature vector. For statistic classifiers the classification accuracy is at most 90.7% and with artificial neural networks the accuracy is at least 92%. The experimental results illustrated that artificial neural networks are an attractive alternative to conventional statistic techniques when dealing with classification task. Moreover, the feature vector based on fractal geometry and wavelet transform can provide good discriminant ability for ultrasonic liver images under study.

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