scholarly journals NEURAL NETWORK CLASSIFIER OF ENERGY FACILITIES OPERATING MODES AND ITS RECOGNITION ABILITY ASSESSMENT AT DIFFERENT NUMBER OF PRECEDENTS

2021 ◽  
pp. 45-52
Author(s):  
Yuri A. Dementiy ◽  
Aleksandr N. Maslov
Keyword(s):  
Neural Network ◽  
Electric Power Industry ◽  
Training Sample ◽  
Neural Network Classifier ◽  
Operating Modes ◽  
The Neural Network ◽  
Recognition Ability ◽  
Short Circuits ◽  
Protected Zone

Classical algorithms of relay protection construction do not use all available information base and therefore cannot provide the highest possible sensitivity with guaranteed selectivity. These algorithms, as a rule, concentrate different information, as a result of which it is partially lost. For example, the resistance relay operates with complex resistance, that is, two real parameters, although two complex variables – voltage and current – are used to calculate the complex resistance. This paper shows the solution to the problem of classification of power line operating modes using a neural network algorithm. The simplest neural network, a perceptron, is a universal classifier, since a convergence theorem has been proved for it, showing that if a classification exists, a perceptron of sufficient complexity is able to describe it. The statistical and geometrical interpretations of various algorithms are discussed. The dependence of the quality of the classifier’s work on the distribution of precedents in the training sample, on which the training is based, as well as on the structure and parameters of the neural network, is shown. The recognition ability of the neural network classifier, i.e. the ability to distinguish short circuits within the protected zone from short circuits outside the protected zone at different number of precedents in the training sample, is evaluated. The limits of applicability of such algorithms to the task of classification of object operation modes in electric power industry are shown and recommendations for their practical application are formulated. The results obtained indicate the need to develop methods for training classifiers that are based on a source of informative precedents in the form of a simulation model of the object.

scholarly journals Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3247
Author(s):  
M. Isabel Dieste-Velasco
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Electronic Circuit ◽  
Machine Learning Techniques ◽  
The Neural Network ◽  
Learning Techniques ◽  
Short Circuits ◽  
Test Points ◽  
The Individual

In this study, machine learning techniques based on the development of a pattern–recognition neural network were used for fault diagnosis in an analog electronic circuit to detect the individual hard faults (open circuits and short circuits) that may arise in a circuit. The ability to determine faults in the circuit was analyzed through the availability of a small number of measurements in the circuit, as test points are generally not accessible for verifying the behavior of all the components of an electronic circuit. It was shown that, despite the existence of a small number of measurements in the circuit that characterize the existing faults, the network based on pattern-recognition functioned adequately for the detection and classification of the hard faults. In addition, once the neural network has been trained, it can be used to analyze the behavior of the circuit versus variations in its components, with a wider range than that used to develop the neural network, in order to analyze the ability of the ANN to predict situations different from those used to train the ANN and to extract valuable information that may explain the behavior of the circuit.

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

scholarly journals Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Minimum Mean Square Error ◽  
Gaussian Kernel ◽  
Neural Network Classifier ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

Sign in / Sign up

Export Citation Format

Share Document