https://oldena.lpnu.ua/handle/ntb/52533| Title: | A Hybrid Neuro-Fuzzy Element: a New Structural Node for Evolving Neuro-Fuzzy Systems |
| Authors: | Hu, Zhengbing Tyshchenko, Oleksii |
| Affiliation: | Central China Normal University University of Ostrava Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Hu Z. A Hybrid Neuro-Fuzzy Element: a New Structural Node for Evolving Neuro-Fuzzy Systems / Zhengbing Hu, Oleksii Tyshchenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 402–406. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Hu Z. A Hybrid Neuro-Fuzzy Element: a New Structural Node for Evolving Neuro-Fuzzy Systems / Zhengbing Hu, Oleksii Tyshchenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 402–406. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | Learning Method Evolving System Computational Intelligence Neuro-Fuzzy Unit Data Stream Processing Hybrid Neuro-Fuzzy System Machine Learning |
| Number of pages: | 5 |
| Page range: | 402-406 |
| Start page: | 402 |
| End page: | 406 |
| Abstract: | A modification of the structure for a neurofuzzy unit was offered which is generally a hybrid system that combines nonlinear synapses and an activation function to form the hybrid system’s output value. The introduced neurofuzzy element is specifically an extension of the common neofuzzy neuron which is upgraded at the expense of application of an additional (contracting) activation function. A particular robust learning procedure is also considered for this case that makes it possible to reduce errors while processing data containing abnormal observations. |
| URI: | https://ena.lpnu.ua/handle/ntb/52533 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] J. Gama, Knowledge Discovery from Data Streams. Boca Raton: Chapman and Hall/CRC, 2010. [2] A. Bifet, R. Gavaldà, G. Holmes, and B. Pfahringer, Machine Learning for Data Streams with Practical Examples in MOA. The MIT Press, 2018. [3] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. Amsterdam: IOS Press, 2010. [4] Ye.V. Bodyanskiy, O.K. Tyshchenko, and D.S. Kopaliani, “An Evolving Connectionist System for Data Stream Fuzzy Clustering and Its Online Learning”, Neurocomputing, vol. 262, pp.41-56, 2017. [5] M. Garofalakis, J. Gehrke, and R. Rastogi (eds.), Data Stream Management. Processing High-Speed Data Streams. Berlin Heidelberg: Springer-Verlag, 2016. [6] R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher, and P. Held, Computational Intelligence. A Methodological Introduction. Berlin: Springer-Verlag, 2013. [7] B.M. Wilamowski and J.D. Irwin, Intelligent Systems. Boca Raton: CRC Press, 2017. [8] J. Kacprzyk and W. Pedrycz (eds.), Springer Handbook of Computational Intelligence. Berlin Heidelberg: Springer-Verlag, 2015. [9] C.L. Mumford and L.C. Jain, Computational Intelligence. Berlin: Springer-Verlag, 2009. [10] Ye. Bodyanskiy, I. Pliss, D. Peleshko, Yu. Rashkevych, and O. Vynokurova, “Hybrid Generalized Additive Wavelet-NeuroFuzzy-System and its Adaptive Learning”, Dependability Engineering and Complex Systems: Proc. of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, Brunow, Poland, pp. 51-61, 2016. [11] D. Graupe, Principles of Artificial Neural Networks (Advanced Series in Circuits and Systems). Singapore: World Scientific Publishing Co. Pte. Ltd., 2007. [12] K.-L. Du and M.N.S. Swamy, Neural Networks and Statistical Learning. London: Springer, 2014. [13] K. Suzuki, Artificial Neural Networks: Architectures and Applications. NY: InTech, 2013. [14] R. Tkachenko and I. Izonin, “Model and Principles for the Implementation of Neural-Like Structures based on Geometric Data Transformations”. Advances in Computer Science for Engineering and Education. ICCSEEA2018. Advances in Intelligent Systems and Computing, 2018, in press. [15] G. Hanrahan, Artificial Neural Networks in Biological and Environmental Analysis. NW: CRC Press, 2011. [16] Ye. Bodyanskiy, O. Tyshchenko, and A. Deineko, “An Evolving Radial Basis Neural Network with Adaptive Learning of Its Parameters and Architecture”, Automatic Control and Computer Sciences, Vol. 49, No. 5, pp. 255-260, 2015. [17] S. Haykin, Neural Networks and Learning Machines (3rd Edition). NJ: Prentice Hall, 2009. [18] R. Tkachenko, P. Tkachenko, I. Izonin, and Y. Tsymbal, “Learningbased image scaling using neural-like structure of geometric transformation paradigm”, Studies in Computational Intelligence, vol. 730, pp. 537–565, 2018. [19] S. Bassis, A. Esposito, F. C. Morabito, Advances in Neural Networks: Computational and Theoretical Issues. Springer International Publishing, 2016. [20] I. Izonin, R. Tkachenko, D. Peleshko, T. Rak and D. Batyuk, "Learning-based image super-resolution using weight coefficients of synaptic connections", Proc. Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT), Lviv, Ukraine, pp. 25-29, 2015. [21] J-S.R. Jang, C.T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, New Jersey: Prentice Hall, 1997. [22] L.X. Wang, Adaptive Fuzzy Systems and Control. Design and Stability Analysis. Upper Saddle River, New Jersey: Prentice Hall, 1994. [23] 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. [24] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, “An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm”, I.J. Intelligent Systems and Applications (IJISA), Vol.7(2), pp.21-26, 2015. [25] 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. [26] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, “A hybrid cascade neural network with an optimized pool in each cascade”, Soft Computing. A Fusion of Foundations, Methodologies and Applications (Soft Comput), Vol.19, No.12, pp.3445-3454, 2015. [27] Zh. Hu, Ye.V. Bodyanskiy, and O.K. Tyshchenko, “A Deep Cascade Neural Network Based on Extended Neo-Fuzzy Neurons and its Adaptive Learning Algorithm”, Proc. of 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, pp.801-805, 2017. [28] Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, and O.O. Boiko, “An Evolving Cascade System Based on a Set of Neo-Fuzzy Nodes”, International Journal of Intelligent Systems and Applications (IJISA), Vol. 8(9), pp.1-7, 2016. [29] Zh. Hu, Ye.V. Bodyanskiy, and O.K. Tyshchenko, “A Hybrid Growing ENFN-Based Neuro-Fuzzy System and its Rapid Deep Learning”, Proc. of 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT’2017), Lviv, Ukraine, pp.514-519, 2017. [30] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, “Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks”, Evolving Systems, Vol.7, No.2, pp.107-116, 2016. [31] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [32] Ye. Bodyanskiy, O. Vynokurova, P. Mulesa, G. Setlak, and I. Pliss, “Fast Learning Algorithm for Deep Evolving GMDH-SVM Neural Network in Data Stream Mining Tasks”, Proc. of 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), pp. 257-262, 2016. [33] A. L. Caterini and D. E. Chang, Deep Neural Networks in a Mathematical Framework. Springer, 2018. [34] J. Heaton, Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. CreateSpace Independent Publishing Platform, 2015. [35] A. Menshawy, Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing Limited, 2018. [36] M. Fullan, J. Quinn, and J. McEachen, Deep Learning: Engage the World Change the World. Corwin, 2017. [37] D. Graupe, Deep Learning Neural Networks: Design and Case Studies. World Scientific, 2016. [38] Y. LeCun, Y. Bengio, G.E. Hinton, “Deep learning”, Nature, vol. 521, pp. 436–444, 2015. [39] J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Networks, no. 61, pp. 85–117, 2015. [40] Y. Bodyanskiy and S. Popov, “Neuro-fuzzy unit for real-time signal processing,” Proc. IEEE East-West Design & Test Workshop (EWDTW’06), Sochi, Russia, pp. 403-406, 2006. [41] Ye. Bodyanskiy, S. Popov, and M. Titov, “Robust learning algorithm for networks of neuro-fuzzy units”, Innovations and Advances in Computer Sciences and Engineering, pp. 343-346, 2010. [42] W. J. J. Rey, Robust Statistical Methods. Berlin-Heidelberg-New York: Springer, 1978. [43] D. S. Chen and R. C. Jain, “A Robust Back Propagation Learning Algorithm for Function Approximation”, IEEE Trans. Neural Networks, vol. 5, pp. 467-479, 1994. [44] A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing. Stuttgart: Teubner, 1993. |
| References (International): | [1] J. Gama, Knowledge Discovery from Data Streams. Boca Raton: Chapman and Hall/CRC, 2010. [2] A. Bifet, R. Gavaldà, G. Holmes, and B. Pfahringer, Machine Learning for Data Streams with Practical Examples in MOA. The MIT Press, 2018. [3] A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. Amsterdam: IOS Press, 2010. [4] Ye.V. Bodyanskiy, O.K. Tyshchenko, and D.S. Kopaliani, "An Evolving Connectionist System for Data Stream Fuzzy Clustering and Its Online Learning", Neurocomputing, vol. 262, pp.41-56, 2017. [5] M. Garofalakis, J. Gehrke, and R. Rastogi (eds.), Data Stream Management. Processing High-Speed Data Streams. Berlin Heidelberg: Springer-Verlag, 2016. [6] R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher, and P. Held, Computational Intelligence. A Methodological Introduction. Berlin: Springer-Verlag, 2013. [7] B.M. Wilamowski and J.D. Irwin, Intelligent Systems. Boca Raton: CRC Press, 2017. [8] J. Kacprzyk and W. Pedrycz (eds.), Springer Handbook of Computational Intelligence. Berlin Heidelberg: Springer-Verlag, 2015. [9] C.L. Mumford and L.C. Jain, Computational Intelligence. Berlin: Springer-Verlag, 2009. [10] Ye. Bodyanskiy, I. Pliss, D. Peleshko, Yu. Rashkevych, and O. Vynokurova, "Hybrid Generalized Additive Wavelet-NeuroFuzzy-System and its Adaptive Learning", Dependability Engineering and Complex Systems: Proc. of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, Brunow, Poland, pp. 51-61, 2016. [11] D. Graupe, Principles of Artificial Neural Networks (Advanced Series in Circuits and Systems). Singapore: World Scientific Publishing Co. Pte. Ltd., 2007. [12] K.-L. Du and M.N.S. Swamy, Neural Networks and Statistical Learning. London: Springer, 2014. [13] K. Suzuki, Artificial Neural Networks: Architectures and Applications. NY: InTech, 2013. [14] R. Tkachenko and I. Izonin, "Model and Principles for the Implementation of Neural-Like Structures based on Geometric Data Transformations". Advances in Computer Science for Engineering and Education. ICCSEEA2018. Advances in Intelligent Systems and Computing, 2018, in press. [15] G. Hanrahan, Artificial Neural Networks in Biological and Environmental Analysis. NW: CRC Press, 2011. [16] Ye. Bodyanskiy, O. Tyshchenko, and A. Deineko, "An Evolving Radial Basis Neural Network with Adaptive Learning of Its Parameters and Architecture", Automatic Control and Computer Sciences, Vol. 49, No. 5, pp. 255-260, 2015. [17] S. Haykin, Neural Networks and Learning Machines (3rd Edition). NJ: Prentice Hall, 2009. [18] R. Tkachenko, P. Tkachenko, I. Izonin, and Y. Tsymbal, "Learningbased image scaling using neural-like structure of geometric transformation paradigm", Studies in Computational Intelligence, vol. 730, pp. 537–565, 2018. [19] S. Bassis, A. Esposito, F. C. Morabito, Advances in Neural Networks: Computational and Theoretical Issues. Springer International Publishing, 2016. [20] I. Izonin, R. Tkachenko, D. Peleshko, T. Rak and D. Batyuk, "Learning-based image super-resolution using weight coefficients of synaptic connections", Proc. Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT), Lviv, Ukraine, pp. 25-29, 2015. [21] J-S.R. Jang, C.T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, New Jersey: Prentice Hall, 1997. [22] L.X. Wang, Adaptive Fuzzy Systems and Control. Design and Stability Analysis. Upper Saddle River, New Jersey: Prentice Hall, 1994. [23] 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. [24] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, "An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm", I.J. Intelligent Systems and Applications (IJISA), Vol.7(2), pp.21-26, 2015. [25] 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. [26] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, "A hybrid cascade neural network with an optimized pool in each cascade", Soft Computing. A Fusion of Foundations, Methodologies and Applications (Soft Comput), Vol.19, No.12, pp.3445-3454, 2015. [27] Zh. Hu, Ye.V. Bodyanskiy, and O.K. Tyshchenko, "A Deep Cascade Neural Network Based on Extended Neo-Fuzzy Neurons and its Adaptive Learning Algorithm", Proc. of 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, pp.801-805, 2017. [28] Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, and O.O. Boiko, "An Evolving Cascade System Based on a Set of Neo-Fuzzy Nodes", International Journal of Intelligent Systems and Applications (IJISA), Vol. 8(9), pp.1-7, 2016. [29] Zh. Hu, Ye.V. Bodyanskiy, and O.K. Tyshchenko, "A Hybrid Growing ENFN-Based Neuro-Fuzzy System and its Rapid Deep Learning", Proc. of 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT’2017), Lviv, Ukraine, pp.514-519, 2017. [30] Ye. Bodyanskiy, O. Tyshchenko, and D. Kopaliani, "Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks", Evolving Systems, Vol.7, No.2, pp.107-116, 2016. [31] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [32] Ye. Bodyanskiy, O. Vynokurova, P. Mulesa, G. Setlak, and I. Pliss, "Fast Learning Algorithm for Deep Evolving GMDH-SVM Neural Network in Data Stream Mining Tasks", Proc. of 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), pp. 257-262, 2016. [33] A. L. Caterini and D. E. Chang, Deep Neural Networks in a Mathematical Framework. Springer, 2018. [34] J. Heaton, Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. CreateSpace Independent Publishing Platform, 2015. [35] A. Menshawy, Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing Limited, 2018. [36] M. Fullan, J. Quinn, and J. McEachen, Deep Learning: Engage the World Change the World. Corwin, 2017. [37] D. Graupe, Deep Learning Neural Networks: Design and Case Studies. World Scientific, 2016. [38] Y. LeCun, Y. Bengio, G.E. Hinton, "Deep learning", Nature, vol. 521, pp. 436–444, 2015. [39] J. Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks, no. 61, pp. 85–117, 2015. [40] Y. Bodyanskiy and S. Popov, "Neuro-fuzzy unit for real-time signal processing," Proc. IEEE East-West Design & Test Workshop (EWDTW’06), Sochi, Russia, pp. 403-406, 2006. [41] Ye. Bodyanskiy, S. Popov, and M. Titov, "Robust learning algorithm for networks of neuro-fuzzy units", Innovations and Advances in Computer Sciences and Engineering, pp. 343-346, 2010. [42] W. J. J. Rey, Robust Statistical Methods. Berlin-Heidelberg-New York: Springer, 1978. [43] D. S. Chen and R. C. Jain, "A Robust Back Propagation Learning Algorithm for Function Approximation", IEEE Trans. Neural Networks, vol. 5, pp. 467-479, 1994. [44] A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing. Stuttgart: Teubner, 1993. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
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| 2018_Hu_Z-A_Hybrid_Neuro_Fuzzy_Element_402-406.pdf | 231.11 kB | Adobe PDF | View/Open | |
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