Abstract
New open box and nonlinear model of Sin and Sigmoid Higher Order Neural Network (SS-HONN) is presented in this paper. A new learning algorithm for SS-HONN is also developed from this study. A time series data simulation and analysis system, SS-HONN Simulator, is built based on the SS-HONN models too. Test results show that every error of SS-HONN models are from 2.1767% to 4.3114%, and the average error of Polynomial Higher Order Neural Network (PHONN), Trigonometric Higher Order Neural Network (THONN), and Sigmoid polynomial Higher Order Neural Network (SPHONN) models are from 2.8128 to 4.9076%. It means that SS-HONN models are 0.1131% to 0.6586% better than PHONN, THONN, and SPHONN models.
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References
Barron, R., Gilstrap, L., Shrier, S.: Polynomial and Neural Networks: Analogies and Engineering Applications. In: Proceedings of International Conference of Neural Networks, New York, vol. 2, pp. 431–439 (1987)
Blum, E., Li, K.: Approximation theory and feed-forward networks. Neural Networks 4, 511–551 (1991)
Ghazali, R., Al-Jumeily, D.: Application of pi-sigma neural networks and ridge polynomial neural networks to financial time series prediction. In: Zhang, M. (ed.) Artificial Higher Order Neural Networks for Economics and Business, pp. 190–211. Information Science Reference (an imprint of IGI Global), Hershey (2009)
Gorr, W.L.: Research prospective on neural network forecasting. International Journal of Forecasting 10(1), 1–4 (1994)
Granger, C.W.J.: Some properties of time series data and their use in econometric model specification. Journal of Econometrics 16, 121–130 (1981)
Granger, C.W.J., Weiss, A.A.: Time series analysis of error-correction models. In: Karlin, S., Amemiya, T., Goodman, L.A. (eds.) Studies in Econometrics. Time Series and Multivariate Statistics, pp. 255–278. Academic Press, San Diego (1983), In Honor of T. W. Anderson
Granger, C.W.J., Lee, T.H.: Multicointegration. In: Rhodes Jr., G.F., Fomby, T.B. (eds.) Advances in Econometrics: Cointegration, Spurious Regressions and Unit Roots, pp. 17–84. JAI Press, New York (1990)
Granger, C.W.J., Swanson, N.R.: Further developments in study of cointegrated variables. Oxford Bulletin of Economics and Statistics 58, 374–386 (1996)
Onwubolu, G.C.: Artificial higher order neural networks in time series prediction. In: Zhang, M. (ed.) Artificial Higher Order Neural Networks for Economics and Business, pp. 250–270. Information Science Reference (an imprint of IGI Global), Hershey (2009)
Psaltis, D., Park, C., Hong, J.: Higher Order Associative Memories and their Optical Implementations. Neural Networks 1, 149–163 (1988)
Redding, N., Kowalczyk, A., Downs, T.: Constructive high-order network algorithm that is polynomial time. Neural Networks 6, 997–1010 (1993)
Selviah, D.R., Shawash, J.: Generalized correlation higher order neural networks for financial time series prediction. In: Zhang, M. (ed.) Artificial Higher Order Neural Networks for Economics and Business, pp. 212–249. Information Science Reference (an imprint of IGI Global), Hershey (2009)
Vetenskapsakademien, K.: Time-series econometrics: Co-integration and Autoregressive Conditional Heteroskedasticity. In: Advanced Information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, pp. 1–20 (2003)
Zhang, M., Murugesan, S., Sadeghi, M.: Polynomial higher order neural network for economic data simulation. In: Proceedings of International Conference on Neural Information Processing, Beijing, China, pp. 493–496 (1995)
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Zhang, M. (2011). Sin and Sigmoid Higher Order Neural Networks for Data Simulations and Predictions. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011. Studies in Computational Intelligence, vol 368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22288-7_4
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DOI: https://doi.org/10.1007/978-3-642-22288-7_4
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