Abstract
In the paper the architecture of Deep Stacking Convex Neuro-Fuzzy System for data stream processing in on-line mode is proposed. The advantage of proposed system is that its layers are formed by multivariate modification of hybrid generalized additive neuro-fuzzy system. Such system is characterized by simplicity of computational implementation, high speed learning, increased approximation properties. For learning of the proposed system both conventional least squares method (including its recurrent version) and specialized learning procedures, which have tracking and smoothing properties are used. Proposed system is aimed at solving of wide range of Data Stream Mining problems, which are connected with processing of nonstationary stochastic and chaotic processes under conditions when information is fed to the system in on-line mode.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521, 436–444 (2015)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, New York (2016)
Peleshko, D., Ivanov, Y., Sharov, B., Izonin, I., Borzov, Y.: Design and implementation of visitors queue density analysis and registration method for retail videosurveillance purposes. In: 2016 IEEE First International Conference on Data Stream Mining and Processing, Lviv, Ukraine, pp. 159–162 (2016)
Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)
Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence. A Methodological Introduction. Springer, Berlin (2013)
Du, K.-L., Swamy, M.N.S.: Neural Networks and Statistical Learning. Springer, London (2014)
Nelles, O.: Nonlinear Systems Identification. Springer, Berlin (2001)
Hastie, T.J., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Science+Business Media, LLC, N.Y. (2009)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)
Jang, R.J.-S.: ANFIS: adaptive network based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 116–132 (1993)
Abiyev, R., Kaynak, O.: Fuzzy wavelet neural networks for identification and control of dynamic plants – a novel structure and a comparative study. IEEE Trans. Ind. Electron. 55(2), 3133–3140 (2008)
Bodyanskiy, Y., Vynokurova, O.: Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification. Inf. Sci. 220, 170–179 (2013)
Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Advances Neural Information Processing Systems, pp. 524–532. Morgan Kaufman, San Mateo (1990)
Bodyanskiy, Y., Tyshchenko, O., Kopaliani, D.: A multidimensional cascade neuro-fuzzy system with neuron pool optimization in each cascade. Int. J. Inf. Technol. Comput. Sci. 6(8), 11–17 (2014)
Bodyanskiy, Y., Tyshchenko, O.: Fast deep learning for cascade neuro-fuzzy system. In: 2nd International Workshop and Networking Event “Advances in Data Science”, Holny Mejera, Poland, pp. 18–19 (2016)
Bodyanskiy, Y., Pliss, I., Peleshko, D., Vynokurova, O.: Adaptive deep learning of multivariate hybrid system of computational intelligence. In: VIIth International Conference on Decision-Making Theory, Uzhhorod, Ukraine, pp. 58–59 (2016). (In Ukrainian)
Wang, L.-X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice Hall, Upper-Saddle River (1994)
Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior. In: 2nd International Conference on Fuzzy Logic and Neural Networks, IIZUKA 1992, Iizuka, Japan, pp. 477–483 (1992)
Miki, T., Yamakawa, T.: Analog implementation of neo-fuzzy neuron and its on-board learning. In: Mastorakis, N.E. (ed.) Computational Intelligence and Application, pp. 144–149. WSES Press, Piraeus (1999)
Bodyanskiy, Y., Mulesa, P., Setlak, G., Pliss, I., Vynokurova, O.: Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks. In: 2016 IEEE First International Conference on Data Stream Mining and Processing (DSMP), pp. 257–262 (2016)
Bodyanskiy, Y., Setlak, G., Pliss, I.P., Vynokurova, O.: Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm. In: Xth IEEE International Science and Technology Conference on Computer Science and Information Technologies, Lviv, Ukraine, pp. 111–114 (2015)
Lee, S.-H., Lee, J.-G., Moon, K.-I.: Smart home security system using multiple ANFIS. Int. J. Smart Home 7(3), 121–132 (2013)
Bodyanskiy, Y., Peleshko, D., Setlak, G., Mulesa, P., Vynokurova, O.: Adaptive multivariate generalized additive neuro-fuzzy systems and its on-board fast learning. Neurocomputing 230, 409–416 (2017)
Bodyanskiy, Y., Peleshko, D., Tatarinova, Yu., Vynokurova, O: Architecture of hybrid generalized additive neuro-fuzzy system in modelling technological process. In: XIIIth IEEE International Conference on The Experimental of Designing and Application of CAD Systems in Microelectronics, Lviv-Polyana, Ukraine, pp. 333–335 (2015)
Bodyanskiy, Y., Setlak, G., Peleshko, D., Vynokurova, O.: Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms. In: The 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Warsaw, Poland, pp. 328–333 (2015)
Landim, R.P., Rodrignes, B., Silva, S.R., Matos, W.: A neo-fuzzy-neuron with real-time training applied to flux observer for an induction motor. In: Vth Brasilian Symposium on Neural Networks, pp. 67–72. IEEE Computer Society, Los Alamitos (1998)
Silva, A.M., Caminhas, W., Lemos, A., Gomide, F.: A fast learning algorithm for evolving neo-fuzzy neuron. Appl. Soft Comput. J. 14, 194–209 (2014)
Hastie, T.J., Tibshrani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990)
Bossley, K.M., Brown, M., Harris, C.J.: Neuro-fuzzy model construction for modelling of non-linear processes. In: 3rd European Control Conference, Italy, vol. 3, pp. 2438–2443 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D., Rashkevych, Y. (2018). Deep Stacking Convex Neuro-Fuzzy System and Its On-line Learning. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Advances in Dependability Engineering of Complex Systems. DepCoS-RELCOMEX 2017. Advances in Intelligent Systems and Computing, vol 582. Springer, Cham. https://doi.org/10.1007/978-3-319-59415-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-59415-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59414-9
Online ISBN: 978-3-319-59415-6
eBook Packages: EngineeringEngineering (R0)