Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 29–33 (2018)
Abstract. A deep evolving stacking convex neo-fuzzy network is proposed. It is a feedforward cascade hybrid system, the layers-stacks of which are formed by generalized neo-fuzzy neurons that implement Wang--Mendel fuzzy reasoning. The optimal in the sense of speed algorithms are proposed for its learning. Due to independent layer adjustment, parallelization of calculations in non-linear synapses and optimization of learning processes, the proposed network has high speed that allows to process information in online mode.
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015. http://dx.doi.org/10.1038/nature14539
- J. Schmidhuber, “Deep Learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, 2015. http://dx.doi.org/10.1016/j.neunet.2014.09.003
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
- D. Graupe, Deep Learning Neural Networks. Design and Case Studies. Singapore : World Scientific, 2016. http://dx.doi.org/10.1142/10190
- A. Ivakhnenko, “The group method of data handling – a rival of the method of stochastic approximation,” Soviet Automatic Control, vol. 13, no. 3, pp. 43-55, 1968.
- A. Ivakhnenko, “The group method of data handling – a rival of the method of stochastic approximation,” Automatica, vol. 6, no. 2, pp. 207-219, 1970.
- N. Kasabov, Evolving Connectionist Systems. Springer-Verlag London, 2007. http://dx.doi.org/10.1007/978-1-84628-347-5
- E. Lughofer, Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications. Springer Berlin, 2011. http://dx.doi.org/10.1007/978-3-642-18087-3
- G. Setlak, Ye. Bodyanskiy, O. Vynokurova, and I. Pliss, “Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks,” in Proc. 2016 Federated Conf. on Computer Science and Information Systems (FedCSIS), Gdansk, Poland, pp. 141-145, 2016. http://dx.doi.org/10.15439/2016F183
- Ye. Bodyanskiy, O. Vynokurova, I. Pliss, G. Setlak, and P. Mulesa, “Fast learning algorithm for deep evolving GMDH-SVM neural network in Data Stream Mining tasks,” in Proc. First IEEE Conf. on Data Stream Mining & Processing, Lviv, Ukraine, pp. 318-321, 2016. http://dx.doi.org/10.1109/DSMP.2016.7583555
- A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, Amsterdam: IOS Press, 2010. http://dx.doi.org/10.3233/978-1-60750-472-6-i
- C. C. Aggarwal, Data Streams: Models and Algorithms (advances in database systems), New York: Springer, 2007. http://dx.doi.org/10.1007/978-0-387-47534-9
- S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural Information Processing Systems, D. S. Touretzky Ed. San Mateo, CA : Morgan Kaufman, pp. 524–532, 1990.
- Y. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, “A hybrid cascade neural network with an optimized pool in each cascade,” Soft Computing, 19, No12, pp. 3445-3454, 2015. http://dx.doi.org/10.1007/s00500-014-1344-3
- Y. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, “Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks,” Evolving Systems, 7, No2, pp. 107-116, 2016. http://dx.doi.org/10.1007/s12530-016-9149-5
- T. Yamakawa, E. Uchino, T. Miki, and H. Kusanagi, “A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior,” in Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks, pp. 477-483, 1992.
- E. Uchino and T. Yamakawa, “Soft computing based signal prediction, restoration and filtering,” Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, Boston: Kluwer Academic Publisher, pp. 331-349, 1997. http://dx.doi.org/10.1007/978-1-4615-6191-0_14
- T. Miki and T. Yamakawa, “Analog implementation of neo-fuzzy neuron and its on-board learning,” Computational Intelligence and Applications, Piraeus: WSES Press, pp. 144-149, 1999.
- Ye. Bodyanskiy, I. Pliss, D. Peleshko, and O. Vynokurova, “Deep hybrid system of computational intelligence for time series prediction,” Int. J. “Information Theories and Applications”, 24, No1, pp. 35-49, 2017.
- Ye. Bodyanskiy, O. Vynokurova, I. Pliss, D. Peleshko, and Yu. Rashkevych, “Deep stacking convex neuro-fuzzy system and its online learning,” Advances in “Intelligent Systems and Computing”, vol. 582, Cham, Springer, pp. 49-59, 2018.
- Y. Bodyanskiy, G. Setlak, D. Peleshko, and O. Vynokurova, “Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms,” in Proc. 2015 IEEE 8th Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications “IDAACS 2015”, pp. 328-333, 2015. http://dx.doi.org/10.1109/IDAACS.2015.7340753
- Y. Bodyanskiy, O. Vynokurova, G. Setlak, and I. Pliss, “Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm,” in Proc. Int. Conf. on Computer Sciences and Information Technologies “CSIT 2015”, pp. 111-114, 2015. http://dx.doi.org/10.1109/STC-CSIT.2015.7325445
- Y. Bodyanskiy, O. Vynokurova, I. Pliss, D. Peleshko, and Y. Rashkevych, “Hybrid generalized additive wavelet-neuro-fuzzy- system and its adaptive learning,” Advances in Intelligent Systems and Computing, vol. 470, Cham, Springer, pp. 51-61, 2016. http://dx.doi.org/10.1007/978-3-319-39639-2_5
- Y. Bodyanskiy, O. Vynokurova, G. Setlak, D. Peleshko, and P. Mulesa, “Adaptive multivariate hybrid neuro-fuzzy system and its on-board fast learning,” Neurocomputing, 230, pp. 409-416, 2017. http://dx.doi.org/10.1016/j.neucom.2016.12.042
- Y. Bodyanskiy, O. Vynokurova, I. Pliss, and D. Peleshko, “Hybrid adaptive systems of computational intelligence and their on-line learning for green IT in energy management tasks,” Studies in Systems, Decision and Control, vol. 74, pp. 229-244, 2017. http://dx.doi.org/10.1007/978-3-319-44162-7_12
- T. Hastie and R. Tibshirani, Generalized Additive Models, Chapman and Hall / CRC, 1990.
- D. Wolpert, “Stacked generalization,” Neural Networks, vol. 5, No2, pp. 241-259, 1992. http://dx.doi.org/10.1016/S0893-6080(05)80023-1
- L. Deng, D. Yu, and J. Platt, “Scalable stacking and learning for building deep architectures,” in 2012 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 2133-2136, 2012. http://dx.doi.org/10.1109/ICASSP.2012.6288333
- R. P. Landim, B. Rodrigues, S. R. Silva, and W. M. Caminhas, “A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor,” in Proc. Vth Brazilian Symposium on Neural Networks, pp. 67-72, 1998. http://dx.doi.org/10.1109/SBRN.1998.730996
- L. Deng and D. Yu, “Deep convex net: a scalable architecture for speech pattern classification,” in Proc. of Annual Conference of the International Speech Communication Association (Interspeech), pp. 2285-2288, 2011.
- Ye. Bodyanskiy, V. Kolodyazhniy, and A. Stephan, “An adaptive learning algorithm for a neuro-fuzzy network,” Lecture Notes in Computer Science 2206, Berlin – Heidelberg – New York, Springer, pp. 68-75, 2001. http://dx.doi.org/10.1007/3-540-45493-4_11
- P. Otto, Ye. Bodyanskiy, and V. Kolodyazhniy, “A new learning algorithm for a forecasting neuro-fuzzy network,” Integrated Computer-Aided Engineering, vol. 10, No4, pp. 399-409, 2003.
- O. G. Rudenko, E. V. Bodyanskii, I. P. Pliss, “Adaptive algorithm for prediction of random sequences,” Soviet automatic control, 12, No1, pp. 46-48, 1979.