Open AccessEffect on Neural Pattern Classifier for Intelligent Gas Sensor by Increasing Number of Hidden Layer
Author(s) -
Shikha Srivastava
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37583
Subject(s) - classifier (uml) , computer science , artificial neural network , pattern recognition (psychology) , artificial intelligence , backpropagation , contextual image classification , data classification , tin oxide , perceptron , image (mathematics) , oxide , chemistry , organic chemistry
Neural networks are used to solve complex problem viz., speech and image recognition, pattern recognition (Pattern classification), computer vision etc. Pattern classification by using Back Propagation algorithm for an intelligent gas sensor application is presented. The classifier is trained using published data of thick film tin oxide sensor array. Its superior classification and learning performance is demonstrated for discrimination of alcohols and alcoholic beverages by increasing number of hidden layer. The new model proposed in this article give steep and monotone learning curve and better classification efficiency. Keywords: Neural Network classifier, Back Propagation Algorithm, system error, classification efficiency, learning curve