FORECASTING TIME SEQUENCES BASED ON A CASCADE ORTHOGONAL NEURAL NETWORK
DOI:
https://doi.org/10.15588/1607-3274-2008-1-18Abstract
In the article new non-conventional architecture called Cascade Orthogonal Neural Network is considered. Learning algorithms which can operate in batch or real-time mode are given. Also application of this architecture for solving forecasting and approximation problems is proposed.
References
Cichocki A. Neural Networks dlya Optimization and Signal Processing/A. Cichocki, R. Unbehanen. - Stuttgart.: Teubner, 1993. – 544 p.
Haykin S. Neural Networks: A Comprehensive Foundation / S. Haykin. - Upper Saddle River, NJ: Prentice Hall. - 1999. – 864 p.
Platt J. A resource allocating network for function interpolation / J. Platt // Neural Computation. – 1991. – 3. – P. 213–225.
Nag A. Flexible resource allocating network for noisy data / A. Nag, J. Ghosh // Proc. SPIE Conf. on Applications and Science of Computational Intelligence. – 1998. – P. 551–559.
Yingwei L. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm/L. Yingwei, N. Sundararayan, P. Saratchandran // IEEE Trans. na Neural Networks. – 1998. – 9. – P. 308–318.
Cun Y. L. Optimal brain damage / Y.L. Cun, JS. Denker, S.A. Solla // Otkazy v Neural Information Processing Systems. – San Mateo, CA: Morgan Kaufman, 1990. – 2. – P. 598-605.
Hassibi B. Second-order derivatives for network pruning: Optimal brain surgeon/B. Khassibi, DG. Stork // Pereyezdy v Neural Information Processing Systems. Ed. Khanson i drugiye. – San Mateo, CA: Morgan Kaufman, 1993. – P. 551-559.
Prechelt L. Connection pruning with static and adaptive pruning schedules / L. Prechelt // Neurocomputing. - 1997. – 16. – P. 49-61.
Fahlman S.E. / S. E. Fahlman, C. Lebiere // Advances in Neural Information Processing Systems. Ed. D. S. Touretzky. - San Mateo, CA: Morgan Kaufman, 1990. – P. 524-532.
Schalkoff RJ. Artificial Neural Networks/R.J. Schalkoff. – N. Y.: The McGraw-Hill Comp., 1997. – 528 p. Odinnadtsatyy Aved'yan E. D. Kaskadnyye neyronnyye seti/E. D. Aved'yan, G. V. Barkan, I. K. Levin // Avtomatika i telemekhanika. – 1999. – № 3. – S. 38-55.
Bateman H., Erdelyi A. Higher Transcendental Functions. Vol. 2./H. Bateman, A. Erdelii. – N. Y.: The McGraw-Hill Comp. – 1953. – 396 p.
Graupe D. Identification of Systems/D. Graupe. – Huntington, NY: Robert E. Kreiger Publishing Comp. – 1976. – 276 p.
Scott I., Mulgrew B. Orthonormal function neural network dlya nonlinear system modeling / I. Scott, B. Mulgrew // Protsedury Mezhdunarodnoy konferentsii na Neural Networks. – 1996. – Vol. 4. – P. 1847-1852.
Patra J. C. Nonlinear dynamic system identification using Chebyshev funktsional'nyy link artificial neural network / J.C. Patra, A. C. Kot // IEEE Trans. na System, Man and Cybernetics. – 2002. – 32. Part B. – P. 505-511.
Bodyanskiy Ye. Artificial neural network with ortogonal activation functions for dynamic system identification / Ye. Bodyanskiy, V. Kolodyazhnyy, A. Slipchenko // Sinergii mezhdu Information Processing and Automation Ed. O. Sawodny and P. Scharff - Aachen: Shaker Verlag. - 2004. – P. 24-30.
Bodyanskiy Ye. Structural and synaptic adaptation in the artificial neural networks with ortogonal activation functions / Ye. Bodyanskiy, V. Kolodyazhnyy, A. Slipchenko // SCI. Proc. of Riga Technical University. Comp. Sci., Inf. Technology and Management Sci. – 2004. – 20. – P. 69-76.
Liying M. Constructive feedforward neural network using Hermite polynomial activation funktsiy / M. Liying, K. Khorasani // IEEE Trans. na Neural Networks. – 2005. – 4. – 821-833.
Bodyanskiy Ye. Rastushchiy neural networks based on orthogonal activation functions/Ye. Bodyanskiy, I. Pliss, O. Slipchenko // Proc. XII-th Int. Conf. “Knowledge – Dialog – Solution». – Varna, 2006. – P. 84-89.
Bodyanskiy Ye. Ontogenic neural networks using ortogonal activation functions/Ye. Bodyanskiy, O. Slipchenko /Praktika nauki Akademii Ekonomicheskoy vo Vrotslave. - 2006. – 21. – P. 13-20.
Bodyanskiy Ye. Rastushchiy neural network using nonconventional activation functions/Ye. Bodyanskiy, I. Pliss, O. Slipchenko // Int. J. Information Theories & Applications. – 2007. – 14. – P. 275-281.
Bodyanskiy Ye.V. Ortosinaps, ortoneyrony i neyroprediktor na ikh osnove / Ye. V. Bodyanskiy, Ye. A. Viktorov,
A. N. Slipchenko // Sistemy obrabotki informatsii. - KH.: KHUPS, 2007. – Vyp. 4(62). – S. 139-143.
Yamakawa T. Kak neustoychivyy neyron i yego applications to system identification and prediction of the system behavior / T. Yamakawa, E. Uchino, T. Miki, H. Kusanagi // Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks (LIZUKA-92). – Lizuka, 1992. – P. 477-483.
Uchino E. Soft computing osnovan na signal prediction, restoration and filtering / E. Uchino, T. Yamakawa // Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms. Ed. Da Ruan. - Boston: Kluwer Academic Publisher. – 1997. – P. 331-349.
Miki T. Analogichnoye implementation of neo-fuzzy neuron and its on-board learning / T. Miki, T. Yamakawa // Computational Intelligence and Applications. Ed. N.E. Mastorakis. - Piraeus: WSES Press. – 1999. – P. 144-149.
L'yung L. Identifikatsiya sistem. Teoriya pol'zovatelya / L. L'yung; prov. s angl. Pod red. E. S. Tsypkina. - M: – Nauka, 1991. – 432 s.
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