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Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

Hussain, A and Al-Jumeily, D and Al-Askar, H and Radi, N (2015) Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction. Neurocomputing. ISSN 1872-8286

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Abstract

This paper presents a novel type of recurrent neural network, the regularized dynamic self-organized neural network inspired by the immune algorithm. The regularization technique is used with the dynamic self-organized multilayer perceptrons network that is inspired by the immune algorithm. The regularization has been addressed to improve the generalization and to solve the over-fitting problem. In this work, the average values of 30 simulations generated from 10 financial time series are examined. The results of the proposed network were compared with the standard dynamic self-organized multilayer perceptrons network inspired by the immune algorithm, the regularized multilayer neural networks and the regularized self-organized neural network inspired by the immune algorithm. The simulation results indicated that the proposed network showed average improvement using the annualized return for all signals of 0.491, 8.1899 and 1.0072 in comparison to the benchmarked networks, respectively.

Item Type: Article
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering, 17 Psychology And Cognitive Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Elsevier
Date Deposited: 29 Apr 2016 10:52
Last Modified: 17 Dec 2016 00:50
URI: http://researchonline.ljmu.ac.uk/id/eprint/2800

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