FLEXIBLE WAVELET-NEURO-FUZZY NEURON IN DYNAMIC DATA MINING TASKS
Keywords:
flexible neo-fuzzy neuron, flexible activation-membership function, learning algorithm, forecasting, identificationAbstract
A new flexible modification of neo-fuzzy neuron (FNFN) and adaptive learning algorithms for the tuning of its all parameters are proposed in the paper. The algorithms are interesting in that they provide on-line tuning of not
only the synaptic weights and membership functions parameters, but also forms of these functions, that provide improving approximation properties and allow to avoid the occurrence of ”gaps” in space of inputs. The proposed
algorithms have both the tracking and filtering properties, so the FNFN can be effectively used for prediction, filtering and smoothing of non-stationary stochastic and chaotic sequences. A special feature of the proposed approach
is its computational simplicity in comparison with known learning procedures for hybrid wavelet-neuro-fuzzy systems of computational intelligence.
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References
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11 G.C. Goodwin, P.J. Ramadge, P.E. Caines, A globally convergent adaptive predictor, Automatica, 17(1), (1981), 135-140.
12 Ye.V. Gorshkov, V.V. Kolodyazhniy, I.P. Pliss, Adaptive learning algorithm for a neo-fuzzy neuron and neuro-fuzzy network based on a polynomial membership functions, Bionica Intellecta, 61(1), (2004), 78-81.
13 Ye. Bodyanskiy, Ye. Viktorov, The cascade neo-fuzzy architecture using cubic spline activation functions, Int. J. Information Theories and Application, 16(3), (2009), 245-259.
14 V. Kolodyazhniy, Ye. Bodyanskiy, Cascaded multiresolution spline-based fuzzy neural network, Eds. P. Angelov, D. Filev, N.Kasabov, in Proc. Int. Symp. on Evolving Intelligent Systems, Leicester, UK, (2010), 26-29.
15 Ye. Bodyanskiy, N. Lamonova, I. Pliss, O. Vynokurova, An adaptive learning algorithm for a wavelet neural network, Expert Systems, 22(5), (2005), 235-240.
16 Ye. Bodyanskiy, O. Vynokurova, Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification. Information Science,
220, (2013), 170-179.
2 E. Lughofer, Evolving Fuzzy Systems: Methodologies, Advanced Concepts and Applications, Springer, 2011.
3 Abiyev R.H., Kaynak O.: Fuzzy wavelet neural networks for identification and control of dynamic plants - A novel structure and a comparative study. IEEE Trans. on Industrial Electronics, 55(8) 3133–3140 (2008)
4 Ye. Bodyanskiy, I. Pliss, O. Vynokurova, Adaptive wavelet-neuro-fuzzy network in the forecasting and emulation tasks, Int. J. on Information Theory and Applications, 15 (1), (2008), 47-55.
5 Ye. Bodyanskiy, I. Pliss, O. Vynokurova, Hybrid wavelet-neuro-fuzzy system using adaptive W-neurons. Wissenschaftliche Berichte, FH Zittau/ Goerlitz, 106(N.24542490), (2008), 301–308.
6 Ye. Bodyanskiy, I. Pliss, O. Vynokurova, Hybrid GMDH-neural network of computational intelligence, in: Proc. 3rd International Workshop on Inductive Modelling, Poland, Krynica, (2009), 100-107.
7 T. Miki, T. Yamakawa, Analog implementation of neo-fuzzy neuron and its onboard learning, In N.E. Mastorakis, editor, Computational Intelligence and Application. WSES Press, (1999), 144- 149.
8 T. Yamakawa, T. Miki, E. Uchino, H. Kusanagi, A neo fuzzy neuron and its applications to system identification and prediction of the system behavior, in: Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks - ”IIZUKA-92”, Iizuka, Japan, (1992), 477-483.
9 E. Uchino, T. Yamakawa, Soft computing bases signal prediction, restoration, and filtering, in Da Ruan, editor, Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, Boston, Kluwer Academic Publishers, (1997), 331- 349.
10 Ye. Bodyanskiy, I. Kokshenev, V. Kolodyazhniy, An adaptive learning algorithm for a neo fuzzy neuron, in Proc. 3-nd Int. Conf. of European Union Society for Fuzzy Logic and Technology (EUSFLAT’03), Zittau, Germany, (2003),
375-379.
11 G.C. Goodwin, P.J. Ramadge, P.E. Caines, A globally convergent adaptive predictor, Automatica, 17(1), (1981), 135-140.
12 Ye.V. Gorshkov, V.V. Kolodyazhniy, I.P. Pliss, Adaptive learning algorithm for a neo-fuzzy neuron and neuro-fuzzy network based on a polynomial membership functions, Bionica Intellecta, 61(1), (2004), 78-81.
13 Ye. Bodyanskiy, Ye. Viktorov, The cascade neo-fuzzy architecture using cubic spline activation functions, Int. J. Information Theories and Application, 16(3), (2009), 245-259.
14 V. Kolodyazhniy, Ye. Bodyanskiy, Cascaded multiresolution spline-based fuzzy neural network, Eds. P. Angelov, D. Filev, N.Kasabov, in Proc. Int. Symp. on Evolving Intelligent Systems, Leicester, UK, (2010), 26-29.
15 Ye. Bodyanskiy, N. Lamonova, I. Pliss, O. Vynokurova, An adaptive learning algorithm for a wavelet neural network, Expert Systems, 22(5), (2005), 235-240.
16 Ye. Bodyanskiy, O. Vynokurova, Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification. Information Science,
220, (2013), 170-179.
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Published
11.11.2013
How to Cite
Bodyanskiy, Y. .-., Pliss, I. .-., & Vynokurova, O. .-. (2013). FLEXIBLE WAVELET-NEURO-FUZZY NEURON IN DYNAMIC DATA MINING TASKS. Oil and Gas Power Engineering, (2(20), 158–162. Retrieved from https://nge.nung.edu.ua/index.php/nge/article/view/269
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SCIENCE AND MODERN TECHNOLOGIES
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