Open AccessIdentification of Non-linear Systems through Convolutional Neural Network
Author(s) -
P. Rajendra,
A. Subbarao,
G. Ramu,
Rahul Boadh
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5058.098319
Subject(s) - convolutional neural network , computer science , identification (biology) , noise (video) , artificial intelligence , test data , nonlinear system , system identification , pattern recognition (psychology) , artificial neural network , machine learning , data modeling , image (mathematics) , botany , physics , quantum mechanics , database , biology , programming language
The theory of control systems deals with the analysis and design of interacting components of a system in a configuration that provides the desired behavior. This paper deals with the problem of the identification of non-linear systems through Convolutional Neural Network (CNN). We propose a structure of a CNN and perform simulations with test data using unsupervised learning for the identification of nonlinear systems. Also, MLP is used to compare the results when there is noise in the training data, which allows us to see that the proposed CNN has better performance and can be used for cases where the noise is present. The proposed CNN is validated with test data. Tests are carried out with Gas oven data, comparing the proposed structure of CNN with a MLP. When there is noise in the data, CNN has better performance than MLP.