Journal of Soft Computing Paradigm
IRO Journals
Volume 1 — Issue 1 — September 2019

RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES

https://doi.org/10.36548/jscp.2019.1.004
Jennifer S. Raj
Professor, Department of Electronics & Communication Engineering, Gnanamani College of Technology, Namakal
J. Vijitha Ananthi
Research Analyst, EJesra
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How to Cite

Raj, Jennifer S., and J. Vijitha Ananthi. 2019. “RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES”. Journal of Soft Computing Paradigm 1 (1): 33-40. https://doi.org/10.36548/jscp.2019.1.004.
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Keywords

— Support Vector Machine
— Recurrent Neural Networks
— pattern recognition
— back-propagation algorithm
Published: 30-09-2019

Abstract

The nonlinear regression estimation issues are solved by successful application of a novel neural network technique termed as support vector machines (SVMs). Evaluation of recurrent neural networks (RNNs) can assist in pattern recognition of several real-time applications and reduce the pattern mismatch. This paper provides a robust prediction model for multiple applications. Traditionally, back-propagation algorithms were used for training RNN. This paper predict system reliability by applying SVM learning algorithm to RNN. Comparison of the proposed model is done with the existing systems for analysis of prediction performance. These results indicate that the performance of proposed system exceeds that of the existing ones.

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