scholarly journals Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

2021 ◽  
Author(s):  
Upasana Sahu ◽  
Kushaagra Goyal ◽  
Debanjan Bhowmik
Keyword(s):  
Neural Network ◽  
Data Sets ◽  
Net Energy ◽  
Spiking Neural Network ◽  
Data Set ◽  
Spintronic Devices ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Spike Time Dependent Plasticity ◽  
Learning Schemes

We trained <b>Spiking neural network </b>(SNN) using <b>spike time dependent plasticity (STDP)</b>-enabled learning under two different learning schemes in <b>MNIST data set</b>(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.

scholarly journals Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

2021 ◽  
Author(s):  
Upasana Sahu ◽  
Kushaagra Goyal ◽  
Debanjan Bhowmik
Keyword(s):  
Neural Network ◽  
Data Sets ◽  
Net Energy ◽  
Spiking Neural Network ◽  
Data Set ◽  
Spintronic Devices ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Spike Time Dependent Plasticity ◽  
Learning Schemes

We trained <b>Spiking neural network </b>(SNN) using <b>spike time dependent plasticity (STDP)</b>-enabled learning under two different learning schemes in <b>MNIST data set</b>(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.

scholarly journals Training a digital model of a deep spiking neural network using backpropagation

2020 ◽  
Vol 224 ◽  
pp. 01026
Author(s):  
V Bondarev
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Spiking Neural Network ◽  
Data Set ◽  
Digit Recognition ◽  
Network Training ◽  
Spike Sequences ◽  
Spike Neural Network ◽  
Handwritten Digit ◽  
Vector Matrix

Deep spiking neural networks are one of the promising eventbased sensor signal processing concepts. However, the practical application of such networks is difficult with standard deep neural network training packages. In this paper, we propose a vector-matrix description of a spike neural network that allows us to adapt the traditional backpropagation algorithm for signals represented as spike time sequences. We represent spike sequences as binary vectors. This enables us to derive expressions for the forward propagation of spikes and the corresponding spike training algorithm based on the back propagation of the loss function sensitivities. The capabilities of the proposed vector-matrix model are demonstrated on the problem of handwritten digit recognition on the MNIST data set. The classification accuracy on test data for spiking neural network with 3 hidden layers is equal to 98.14%.

scholarly journals Handwritten Digit Recognition System

2021 ◽  
pp. 76-85
Author(s):  
Shubham Mendapara ◽  
Krish Pabani ◽  
Yash Paneliya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Strategies ◽  
Optical Character Recognition ◽  
Handwritten Digit Recognition ◽  
Neural Network Approach ◽  
Data Set ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Compositional Styles

Recently, handwritten digit recognition has become impressively significant with the escalation of the Artificial Neural Networks (ANN). Apart from this, deep learning has brought a major turnaround in machine learning, which was the main reason it attracted many researchers. We can use it in many applications. The main aim of this article is to use the neural network approach for recognizing handwritten digits. The Convolution Neural Network has become the center of all deep learning strategies. Optical character recognition (OCR) is a part of image processing that leads to excerpting text from images. Recognizing handwritten digits is part of OCR. Recognizing the numbers is an important and remarkable subject. In this way, since the handwritten digits are not of same size, thickness, position, various difficulties are faced in determining the problem of recognizing handwritten digits. The unlikeness and structure of the compositional styles of many entities further influences the example and presence of the numbers. This is the strategy for perceiving and organizing the written characters. Its applications are such as programmed bank checks, health, post offices, for education, etc. In this article, to evaluate CNN's performance, we used the MNIST dataset, which contains 60,000 images of handwritten digits. Achieves 98.85% accuracy for handwritten digit. And where 10% of the total images were used to test the data set.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

scholarly journals Comparison of the efficiency of neural network algorithms in recognition and classification problems

2020 ◽  
Vol 224 ◽  
pp. 01025
Author(s):  
Alexey Beskopylny ◽  
Alexandr Lyapin ◽  
Nikita Beskopylny ◽  
Elena Kadomtseva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computation Time ◽  
Classification Problems ◽  
Handwritten Digit Recognition ◽  
Network Algorithms ◽  
Digit Recognition ◽  
Labour Intensity ◽  
Handwritten Digit ◽  
Accuracy Of Results

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Sign in / Sign up

Export Citation Format

Share Document