scholarly journals Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

2020 ◽  
Vol 167 ◽  
pp. 2445-2457
Author(s):  
Duddela Sai Prashanth ◽  
R Vasanth Kumar Mehta ◽  
Nisha Sharma
Keyword(s):  
Neural Network ◽  
Pattern Recognition

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

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.

Prothesis Movements Pattern Recognition Based on Auto-Regressive Model and Wavelet Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 2156-2161 ◽  
Author(s):  
Cheng Gao ◽  
Jiao Ying Huang ◽  
Wei Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Ar Model ◽  
Wavelet Neural Networks ◽  
Time Frequency ◽  
The Neural Network ◽  
Time Frequency Localization ◽  
Auto Regressive ◽  
The Right

Wavelet neural networks (WNN) combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prothesis movements was studied in this paper. Firstly, an AR model was used to analysis the surface myoelectric signals (SMES) which recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Four types of prosthesis movements are recognized by extracting four-order AR coefficient and construct them as eigenvector into WNN, which was used to study the correlation between SMES and wristwork. This paper compares the classification accuracy of four movements such as hand open (HO), hand close (HC), forearm intorsion (FI) and forearm extorsion (FE).The experimental results show that the proposed method can classify correctly for at least 93.75% of the test data.

scholarly journals A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Andrei Velichko ◽  
Maksim Belyaev ◽  
Petr Boriskov
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Simulated Annealing Algorithm ◽  
Cell Array ◽  
Reference Oscillator ◽  
Oscillatory Neural Network ◽  
A Cell ◽  
Novel Method ◽  
Coupling Mechanisms

The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 × 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.

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