Least Squares Support Vector Machine Based on WaveletNeuron
Abstract
Keywords: 
Adaptive wavelet function; forecasting; least squares support vector machine; nonlinear nonstationary time series; waveletneuron

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References
K.L. Du and M. N. S. Swamy, Neural Networks and Statistical Learning London: SpringerVerlag, 2014. http://dx.doi.org/10.1007/978 1447155713
S. Haykin, Neural Networks. A Comprehensive Foundation Upper Saddle River, N. J.: Prentice Hall, 1999.
J.S. R. Jang, “ANFIS: Adaptivenetworkbased fuzzy inference systems,” IEEE Trans. on Syst. Man. and Cybern., vol. 23, issue 3, pp. 665–685, 1993. http://dx.doi.org/10.1109/21.256541
J.S. R. Jang, C. T. Sun, and E. Mizutani. NeuroFuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, N. J.: Prentice Hall, 1997.
O. Nelles, Nonlinear System Identification, Berlin: Springer, 2001. http://dx.doi.org/10.1007/9783662043233
E. Uchino and T. Yamakawa, “Soft computing based signal prediction, restoration and filtering,” in Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, Da Ruan Eds., Boston: Kluwer Academic Publisher, 1997, pp. 331–349.
Ye. Bodyanskiy and O. Vynokurova, ”Hybrid adaptive waveletneuro fuzzy system for chaotic time series identification,” Information Science, n. 220, pp.170–179, 2013.
L. Ljung, System Identification: Theory for the User, N.Y.: Prentice Hall, 1999.
C. Cortes and V. Vapnik, “Support vector networks,” Machine Learning, n. 20, pp.273–297, 1995. http://dx.doi.org/10.1007/BF00994018
V.N. Vapnik and A.Ya. Chervonenkis, Pattern Recognition Theory (Statistical Learning Problems), М.: Nauka, 1974. (in Russian).
V. N. Vapnik and A. Ya. Chervonenkis, Empirical Data Dependencies Restoration, М.: Nauka, 1979. (in Russian).
V. N. Vapnik, The Nature of Statistical Learning Theory, N. Y.:
Springer, 1995. http://dx.doi.org/10.1007/9781475724400
J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle. Least Squares Support Vector Machines, Singapore: World Scientific, 2002.
S. Abe and D. Tsujinishi, “Fuzzy Least Squares Support Vector Machines for multiclass problems,” Neural Networks, n. 16, pp. 785– 792, 2003.
Ch.F. Lin and Sh.D. Wang, “Fuzzy Support Vector Machines,” IEEE Trans. on Neural Networks, n. 13, pp. 646–671, 2002.
S.M. Pandhiani and A.B. Shabri, “Time Series Forecasting Using WaveletLeast Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data,” Open Journal of Statistics, n. 3, pp. 183–194, 2013.
D. Kumar, R. K. Tripathy and A. Acharya, “Least squares support vector machine based multiclass classification of EEG signals,” WSEAS Transactions on Signal Processing, vol. 10, pp. 86–94, 2014.
D. Zahirniak, R. Chapman, S. K. Rogers, B. W. Suter, M. Kabrisky and V. Pyati, “Pattern recognition using radial basis function network,” in
Application of Artificial Intelligence Conf., Dayton, OH, 1990, pp. 249–260.
T. Yamakawa, “A novel nonlinear synapse neuron model guaranteeing a global minimum – Wavelet neuron,” in Proc. 28th IEEE Int. Symp. On MultipleValued Logic, Fukuoka, Japan, IEEE Comp. Soc., 1998, pp. 335–336.
Ye. Bodyanskiy, O. Vynokurova, and O. Kharchenko, “Hybrid cascade neural network based on waveletneuron,” Information Theories and Application, vol. 18, no. 4, pp. 335–343, 2011.
Ye. Bodyanskiy, N. Lamonova, I. Pliss, and O. Vynokurova, “An adaptive learning algorithm for a wavelet neural network,” Expert Systems, vol. 22, no. 5, pp. 235–240, 2005. http://dx.doi.org/10.1111/j.14680394.2005.00314.x
Ye. Bodyanskiy, I. Pliss, and O. Vynokurova, “Flexible neofuzzy neuron and neurofuzzy network for monitoring of time series properties,” Scientific J. of Riga Technical University. Information Technology and Management Science, vol. 16, pp. 47–52, 2013.
Ye. Bodyanskiy, O. Vynokurova, E. Yegorova, “Radialbasisfuzzy waveletneural network with adaptive activationmembership function” Int. J. on Artificial Intelligence and Machine Learning, no. 8 (II), pp. 9–15, 2008.
G. C. Goodwin, P. J. Ramadge, and R. E. Caines, “A globally convergent adaptive predictor,” Automatica, vol. 17, no. 1, pp. 135–140, 1981. http://dx.doi.org/10.1016/00051098(81)900893
F. R. Gantmacher, The Theory of Matrices AMS, Chelsea Publishing: Reprinted by American Mathematical Society, 2000.
Data Market – the open portal to thousands of datasets [Online]. Available: http://datamarket.com/en/data/set/1loo/#!ds=1loo!1n6s=2qi. 2ql.2qn&display=line&title=Average+monthly+temperatures+across+th e+world+(17012011)&s=8gd
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Copyright (c) 2014 Yevgeniy Bodyanskiy, Olena Vynokurova, Oleksandra Kharchenko
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