Abstract
This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. The induced gradient based learning procedure, adjusts solely the consequent network parameters. To improve the robustness of the network and the possibilities for handling uncertainties, Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing – A Computational Approach to Learning and Machine Intelligence. Prentice Hall (1997)
Jin, Y.: Advanced Fuzzy Systems Design and Applications. Physica (2003)
Borgelt, C., Klawonn, F., Kruse, R., Nauck, D.: Neuro-Fuzzy-Systeme. Von den Grundlagen künstlicher Neuronaler Netze zur Kopplung mit Fuzzy-Systemen. Computational Intelligence (2003)
Diaconescu, E.: The use of NARX Neural Networks to predict Chaotic Time Series. WSEAS Transactions on Computer Research 3(3), 182–191 (2008)
Esmaili, A., Shahbazian, M., Moslemi, B.: Nonlinear process identification using fuzzy wavelet neural network based on particle swarmoptimization algorithm. Journal of Basic Applied Science Research 3(5) (2013)
Jang, J.S.R.: Anfs: Adaptive Network based Fuzzy Inference Systems. IEEE Transactions, System, Man & Cybernetics 23(3), 665–685 (1993)
Bodyanskiy, Y., Kokshenev, I., Kolodyazhniy, V.: An Adaptive Learning Algorithm for a Neo-Fuzzy Neuron. In: Proceedings of the 3rd Conference of the European Society for Fuzzy Logic and Technology, pp. 375–379 (2005)
Bodyanskiy, Y., Pliss, I., Vynokurova, O.: Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties. Information Technology and Management Science 16, 47–52 (2013)
Bodyanskiy, Y., Viktorov, Y.: The cascade Neo-Fuzzy architecture and its online learning algorithm. International Book Series Information Science and Computing 17(1), 110–116 (2010)
Silva, A.M., Caminhas, W., Lemos, A., Gomide, F.: A fast learning algorithm for evolving Neo-Fuzzy neuron. Applied Soft Computing 14, 194–209 (2014)
Kim, H.D.: Optimal Learning of Neo-Fuzzy Structure Using Bacteria Foraging Optimization. In: Proceedings of the ICCA 2005 (2005)
Chaturvedi, K.T., Pandit, M., Srivastava, L.: Modified Neo-Fuzzy neuron-based approach for economic and environmental optimal power dispatch. Applied Soft Computing 8, 1428–1438 (2008)
Camargo, E., Aguilar, J., Rios, A., Rivas, F., Aguilarmartin, J.: A Neo-Fuzzy Approach for Bottom Parameters Estimation in Oil Wells. WSEAS Transactions on Systems and Control 9(4), 445–454 (2009)
De Castro, T.N., Souza, F., Alves, J., Pontes, R., Dos Reis, L., Daher, S.: Neo-fuzzy neuron model for seasonal rainfall forecast: A case study of Ceara’s eight homogenous regions. Journal of Intelligent & Fuzzy Systems 25, 389–394 (2013)
Zaychenko, Y., Gasanov, A.: Investigations of Cascade Neo-Fuzzy Neural Networks in the Problem of Forecasting at the Stock Exchange. In: Proc. of the IVth IEEE International Conference “Problems of Cybernetics and Informatics” (PCI 2012), pp. 227–229 (2012)
Yao, J., Mao, J., Zhang, W.: Application of Fuzzy Tree on Chaotic Time Series Prediction. In: IEEE Proc. of Int. Conf. on Aut. and Logistics, pp. 326–330 (2008)
Lai, Y.: Recent developments in chaotic time series analysis. International Journal of Bifurcation and Chaos 13(6), 1383–1422 (2003)
Diaconescu, E.: The use of NARX Neural Networks to predict Chaotic Time Series. WSEAS Transactions on Ccomputer Rresearch 3(3), 182–191 (2008)
Archana, R., Unnikrishnan, A., Gopikakumari, R.: Bifurcation Analysis of Chaotic Systems using a Model Built on Artificial Neural Networks. In: Proc. of Int. Conf. on Comp. Tech. and Artificial Intelligence, pp. 198–202 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Todorov, Y., Terziyska, M. (2014). Modeling of Chaotic Time Series by Interval Type-2 NEO-Fuzzy Neural Network. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-11179-7_81
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
eBook Packages: Computer ScienceComputer Science (R0)