optimizing the architecture of artificial neural networks in predicting indian stock prices
DESCRIPTION
In forecasting, the design of an Artificial Neural Network (ANN) is a non-trivial task andchoices incoherent with the problem could lead to instability of the network. So a GeneticAlgorithm (GA) approach is used to find an optimal topology for the prediction. This paperpresents a novel approach to Optimization of ANN topology that uses GA for the forecastingof Indian Stock Prices under Bombay Stock Exchange. After determining the optimumnetwork determined by GA, forecasting of the stock prices is found by implementingMATLAB tool. The paper is organized as follows. The first Section deals with theintroduction to Genetic Algorithms; Section two reviews the literature on the optimization ofneural network architectures and applications of genetic algorithms in doing so. Section threegives the proposed approach in the optimization of neural network architectures. Section fourpresents the experimental results by the methodology described in section three and followedby results and conclusion.TRANSCRIPT
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International Journal of Computing Algorithm, Vol 3(3), June 2014 ISSN(Print):2278-2397
Website: www.ijcoa.com
Optimizing the Architecture of Artificial Neural Networks in Predicting Indian Stock Prices
A.Victor Devadoss1, T.Antony Alphonnse Ligori2
1 Head and Associate Professor, Department of Mathematics, Loyola College, Chennai, India.
2 Ph. D Research Scholar, Department of Mathematics, Loyola College, Chennai, India. E-mail: [email protected], [email protected]
Abstract In forecasting, the design of an Artificial Neural Network (ANN) is a non-trivial task and choices incoherent with the problem could lead to instability of the network. So a Genetic Algorithm (GA) approach is used to find an optimal topology for the prediction. This paper presents a novel approach to Optimization of ANN topology that uses GA for the forecasting of Indian Stock Prices under Bombay Stock Exchange. After determining the optimum network determined by GA, forecasting of the stock prices is found by implementing MATLAB tool. The paper is organized as follows. The first Section deals with the introduction to Genetic Algorithms; Section two reviews the literature on the optimization of neural network architectures and applications of genetic algorithms in doing so. Section three gives the proposed approach in the optimization of neural network architectures. Section four presents the experimental results by the methodology described in section three and followed by results and conclusion.