Identification of Non-linear Systems through Convolutional Neural Network
P. Rajendra1, A. Subbarao2, G. Ramu3, Rahul Boadh4
1P. Rajendra*, Department of Mathematics, CMR Institute of Technology, Bengaluru, India.
2A. Subbarao, Dept. of Mathematics, Madanapalle Institute of Technology and Science, Madanapalle, India.
3G. Ramu, Dept. of Mathematics, Madanapalle Institute of Technology and Science, Madanapalle, India.
4Rahul Boadh, School of Basic and Applied Sciences, K.R. Mangalam University, Gurgaon, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 8 August 2019. | Manuscript published on 30 September 2019. | PP: 3429-3434 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5058098319/2019©BEIESP | DOI: 10.35940/ijrte.C5058.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Convolutional Neural Network, Non-linear Systems, Unsupervised Learning
Scope of the Article: Neural Information Processing