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A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange

Asif Perwej, Yusuf Perwej, Nikhat Akhtar, Firoj Parwej


Forecasting is the process of computation in unknown situations from the historical data. Financial forecasting and planning is usually an essential part of the business plan, and would be done as part of setting up the organization and receive funds. Financial forecasting and planning is also an essential activity to confirm a good management, keeping the organization financially sound is a key objective. The prediction of stock market has been a long time tempting topic for researchers from different fields. Stock analysts use various forecasting methods to determine how a stock's price will move in the ensuing day. The purpose of this paper is to explore the radial basis function (RBF) and function linked artificial neural network (FLANN) algorithms for forecasting of financial data. We have based our models on data taken and compared those using historical data from the Bombay Stock Exchange (BSE). The RBF and FLANN parameters updated by Particle swarm optimization (PSO). In this paper, we have examined this algorithm on a number of various parameters including error convergence and the Mean Average Percentage Error (MAPE) and comparative assessment of the RBF and FLANN algorithms is done. The proposed method indeed can help investors consistently receive gains. Finally, a simple merchandise model is established to study the accomplishment of the proposed prediction algorithm against other criterion.

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Chang Pei-Chann,Liu Chen-Hao. A TSK typ e fuzzy rule based system for stock price prediction[J].Expert Systems with Applications.1 (2008) 135-144.

Cao L.J,F.E.H. Tay. Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Trans. Neural Networks.6 (2003) 1506-1518.

Wang Y F. Predicting stock price using fuzzy grey prediction system [J].Expert Systems with Applications.1 (2002) 33-38.

Yusuf Perwej, Asif Perwej, “Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Networks and Genetic Algorithms” for published in the Journal of Intelligent Learning Systems and Applications (JILSA), which is published by Scientific Research Publishing (SRP, USA. Vol. 4, No. 2, May 2012, pages 108-119, ISSN Print: 2150- 8402, ISSN Online: 2150-8410, DOI: 10.4236/jilsa.2012.42010

E.W. Saad , D.V. Prokhorov, D.C. Wunsch (1998), “ Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks”, IEEE Transactions of Neural Network, 9(6), 1456-1470.

. Clarence N.W. Tan and Gerhard E. Wittig, “A Study of the Parameters of a Backpropagation Stock Price Prediction Model”, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems p. 288-91,1993

. Tan, H.; Prokhorov, D.V.; Wunsch, D.C., II; “Conservative thirty calendar day stock prediction using a probabilistic neural network”, Computational Intelligence for Financial Engineering, 1995., Proceedings of the IEEE/IAFE 1995 9-11 April 1995 Page(s):113 – 117

. Yamashita, T.; Hirasawa, K.; Jinglu Hu; “Application of multi-branch neural networks to stock market prediction”, IEEE International Joint Conference on Neural Networks, 2005. IJCNN '05. VoL. 4, Aug. 2005 pp.2544 – 2548 vol. 4.

. Hiemstra, Y.; “A stock market forecasting support system based on fuzzy logic”, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, 1994. Vol. III: Information Systems: Decision Support and Knowledge-Based Systems, Volume 3, 4-7 Jan. 1994 Page(s):281 – 287.

. R.J. Kuo; C.H. Chen, Y.C. Hwang; “An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network”, Fuzzy Sets Systems 118, 2000, pp 21-45.

. Md. Rafiul Hassan , Baikunth Nath, Michael Kirley; “A fusion model of HMM, ANN and GA for stock market forecasting” , Expert Systems with Applications 2006.

. Hyun-jung Kim, Kyung-shik Shin, “A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets” , Applied Soft Computing 2006, March 2006.

Powell M. (1987), Radial basis functions for multivariable interpolation : A review, J.C. Mason and M.G. Cox, eds, Algorithms for Approximation, pp.143-167.

S. Miyoung and P. Cheehang, “A radial basis function approach to pattern recognition and its applications”, ETRI Journal, Volume 22, Number 2, June 2000.

Chen, F. Cowan, and P. Grant (1991), Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, Vol. 2, No. 2, 302-309, 1991

S. Haykin. Adaptive Filter Theory. Third Edition, Prentice Hall, Inc, A Simon & Schuster Company, New Jersey, 1996.

Nikhat Akhtar “ Perceptual Evolution for Software Project Cost Estimation using Ant Colony System ” for published in the International Journal of Computer Applications (IJCA) USA , Volume 81, No.14, Pages 23 – 30, November 2013, ISSN 0975 - 8887,, DOI : 10.5120/14185-2385

J. Kennedy, and R. Eberhart, “Particle Swarm Optimization”, Proceedings in IEEE International Conference Neural Networks, vol. 4, pp. 1942– 1948, 1995.

COMPUSOFT, An international journal of advanced computer technology, 4 (1), January-2015 (Volume-IV, Issue-I)

Dong J and Qi M, “A New Algorithm for Clustering based on Particle Swarm Optimization and K-means”, Proceedings of the International Conference on Artificial Intelligence Computational Intelligence, pp. 264-268, 2009.

Van der Merwe D W and Engelbrecht A P, “Data clustering using particle swarm optimization”, Proceedings of IEEE Congress on Evolutionary Computation, Vol. 1, pp. 215- 220, 2003.

Carlisle, A., and Dozier, G.. (2000). Adapting particle swarm optimization to dynamic environments. Proceedings of International Conference on Artificial Intelligence, 2000 pp. 429-434. Las Vegas, Nevada, USA.

Hu, X., and Eberhart, R. C., and Shi, Y.. (2003). Particle swarm with extended memory for multiobjective optimization. Proc. of 2003 IEEE Swarm Intelligence Symposium, pages 193-197. Indianapolis, Indiana, USA, April 2003. IEEE Service Center.

Parsopoulos, K.E., Vrahatis, M.N.. (2001). Particle swarm optimizer in noisy and continuously changing environments, M.H. Hamza (ed.), Artificial Intelligence and Soft Computing, pp. 289- 294, IASTED/ACTA Press (Anaheim, CA, USA).

Chen, C.L.P. and J.Z. Wan (1999). A Rapid Learning and Dynamic Stepwise Updating Algorithm for Flat Neural Networks and the Application to Time-Series Prediction. IEEE Trans. Systems, Man, and Cybernetics . part B, 29 (1), 62- 72.

Teeter, J., Mo-Yuen, C.: Application of functional link neural network to HVAC thermal dynamic system identification. IEEE Transactions on Industrial Electronics 45(1), 170–176 (1998).

Emrani, S., et al.: Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems. In: 2010 The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), (2010).

Ayoubi, M. (1996). Non-linear System Identification Based on Neural Networks with Locally Distributed Dynamics and Application to Technical Processes. Fortschritt-Berichte VDI, Reihe 8, Nr.591, VDI-Verlag, Düsseldorf.



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