International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 8, No. 10, 8 Oct. 2016
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Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
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Author(s)
Zhengbing Hu
1,*
Yevgeniy V. Bodyanskiy
2
Oleksii K. Tyshchenko
2
Olena O. Boiko
2
Index Terms
Computational Intelligence, time series prediction, neuro-neo-fuzzy System, Machine Learning, ANARX, Data Stream
Abstract
An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.
Cite This Paper
Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko, "Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.10, pp.1-10, 2016. DOI:10.5815/ijitcs.2016.10.01
Reference
[1]Rutkowski L. Computational Intelligence. Methods and Techniques. Springer-Verlag, Berlin-Heidelberg, 2008.
[2]Kruse R, Borgelt C, Klawonn F, Moewes C, Steinbrecher M, Held P. Computational Intelligence. Springer, Berlin, 2013.
[3]Du K-L, Swamy M N S. Neural Networks and Statistical Learning. Springer-Verlag, London, 2014.
[4]Jang J-S, Sun C-T, Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River, 1997.
[5]Rezaei A, Noori L, Taghipour M. The Use of ANFIS and RBF to Model and Predict the Inhibitory Concentration Values Determined by MTT Assay on Cancer Cell Lines. International Journal of Information Technology and Computer Science(IJITCS), 2016, 8(4): 28-34.
[6]Nelles O. Nonlinear System Identification. Springer, Berlin, 2001.
[7]Kasabov N. Evolving fuzzy neural networks – algorithms, applications and biological motivation. Proc. “Methodologies for the Conception, Design and Application of Soft Computing”, Singapore, 1998:271 274.
[8]Kasabov N. Evolving fuzzy neural networks: theory and applications for on-line adaptive prediction, decision making and control. Australian J. of Intelligent Information Processing Systems, 1998, 5(3):154 160.
[9]Kasabov N. Evolving Connectionist Systems. Springer-Verlag, London, 2003.
[10]Lughofer E. Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications. Springer, Berlin, 2011.
[11]Bifet A. Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. IOS Press, 2010, Amsterdam.
[12]Belikov J, Vassiljeva K, Petlenkov E, Nõmm S. A novel Taylor series based approach for control computation in NN-ANARX structure based control of nonlinear systems. Proc. 27th Chinese Control Conference, Kunming, China, 2008:474 478.
[13]Vassiljeva K, Petlenkov E, Belikov J. State-space control of nonlinear systems identified by ANARX and neural network based SANARX models. Proc. WCCI 2010 IEEE World Congress on Computational Intelligence, Barcelona, Spain, 2010:3816 3823.
[14]Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems, 1989, 2:303 314.
[15]Yamakawa T, Uchino E, Miki T, Kusanagi H. A neo fuzzy neuron and its applications to system identification and prediction of the system behavior. Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks “IIZUKA-92”, Iizuka, Japan, 1992:477 483.
[16]Uchino E, Yamakawa T. Soft computing based signal prediction, restoration, and filtering. In: Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, 1997:331 349.
[17]Miki T, Yamakawa T. Analog implementation of neo-fuzzy neuron and its on-board learning. In: Computational Intelligence and Applications, 1999:144 149.
[18]Wang L-X, Mendel J M. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. on Neural Networks, 1992, 3(5):807 814.
[19]Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 1985, 15:116 132.
[20]Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and Systems, 1998, 28:15 33.
[21]Ljung L. System Identification: Theory for the User. Prentice Hall, Inc., Upper Saddle River, 1987.
[22]Polikar R, Alippi C. Learning in nonstationary and evolving environments. IEEE Trans. on Neural Networks and Learning Systems, 2014, 25(1):9 11.
[23]Jin Y, Hammer B. Computational Intelligence in Big Data. IEEE Computational Intelligence Magazine, 2014, 9(3):12 13.
[24]Friedman J, Hastie T, Tibshirani R. The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, Berlin, 2003.
[25]Bodyanskiy Ye, Kolodyazhniy V. Cascaded multiresolution spline-based fuzzy neural network. Proc. Int. Symp. on Evolving Intelligent Systems, Leicester, UK, 2010:26 29.
[26]Bodyanskiy Ye, Kokshenev I, Kolodyazhniy V. An adaptive learning algorithm for a neo-fuzzy neuron. Proc. 3rd Int. Conf. of European Union Soc. for Fuzzy Logic and Technology (EUSFLAT’03), Zittau, Germany, 2003:375 379.
[27]Bodyanskiy Ye, Otto P, Pliss I, Popov S. An optimal algorithm for combining multivariate forecasts in hybrid systems. Lecture Notes in Artificial Intelligence, 2003, 2774:967 973.
[28]Bodyanskiy Ye, Viktorov Ye. The cascade neo-fuzzy architecture using cubic-spline activation functions. Int. J. Information Theories and Applications, 2009, 16(3):245 259.
[29]Bodyanskiy Ye, Teslenko N, Grimm P. Hybrid evolving neural network using kernel activation functions. Proc. 17th Zittau East-West Fuzzy Colloquium, Zittau/Goerlitz, Germany, 2010:39 46.
[30]Bodyanskiy Ye V, Tyshchenko O K, Kopaliani D S. A multidimensional cascade neuro-fuzzy system with neuron pool optimization in each cascade. Int. J. Information Technology and Computer Science, 2014, 6(8):11 17.
[31]Sharkey A J C. On combining artificial neural nets. Connection Science, 1996, 8:299 313.
[32]Bodyanskiy Ye, Deineko A, Stolnikova M. Adaptive generalization of neuro-fuzzy systems ensemble. Proc. of the Int. Conf. “Computer Science and Information Technologies”, Lviv, Ukraine, November 16-19, 2011:13 14.