Abstract
A nonlinear behavior may exist intrinsically with a deterministic dynamic system which shows high sensitivity with initial condition. Such behavior is characterized as chaotic behavior. Strange attractor confines the dynamic behavior of chaotic system in a finite state space region, and the available state variables show the stochastic behavior with time. In this research work, time delay neural network has applied to predict the various chaotic time series. Adaptive social behavior optimization has applied to evolve the optimal set of weights of the time delay neural network. Comparison of learning performance has given with popularly gradient descent-based learning. Performance evaluation has defined in terms of coefficient of determination along with root-mean-square error in prediction under learning and test phase of chaotic time series. The three benchmarks of chaotic time series (logistic differential equation, Mackey–Glass, and Lorenz system) have taken for predicting purpose. We have shown with experimental results that the proposed new method of neural network learning is very efficient and has delivered the better prediction for various complex chaotic time series.
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
Bermolen, P., Rossi, D.: Support vector regression for link load prediction. Comput. Netw. 53, 191–201 (2009)
Lee, C.M., Ko, C.N.: Time series prediction using RBF neural networks with a nonlinear time-varying evolutin PSO algorithm. Neurocomputing 73, 449–460 (2009)
Xu, K., Xu, J.W., Ban, X.J.: Forecasting of some non-stationary time series based on wavelet decomposition. Acta Electronica Sinica 29(4), 566–568 (2001)
Geva, A.B.: ScaleNet-multiscale neural-network architecture for time series prediction. IEEE Trans. Neural Netw. 9(5), 1471–1482 (1998)
Chen, Y.H., Yang, B., Dong, J.W.: Time-series prediction using a local linear wavelet neural network. Neurocomputing 69, 449–465 (2006)
Szpiro, G.G.: Forecasting chaotic time series with genetic algorithms. Am. Phys. Soc. 2557–2568 (1997)
Lamamra, K, Azar, A.T., Ben Salah, C.: Chaotic system modelling using a neural network with optimized structure. Studies in Computational Intelligence, vol. 688, pp 833–856. Springer, Cham (2017)
Oliveira, K.D., Vannucci, A., da Silva, E.C.: Using artificial neural networks to forecast chaotic time series. Phys. A 284, 393–404 (2000)
Thissen, U., van Brakela, R., de Weijer, A.P., Melssen, W.J., Buydens, L.M.C.: Using support vector machines for time series prediction. Chemometr. Intell. Lab. Syst. 69, 35–49 (2003)
Volná, E., et al.: Nonlinear time series analysis via neural networks. In: Chaos and Complex Systems, pp. 415–418. Springer, Heidelberg (2013)
Ristanoski, G., Liu, W., Bailey J.: Time series forecasting using distribution enhanced linear regression. In: Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7818. Springer, Heidelberg (2013)
Volna, E., Kotyrba, M.: Time series prediction based on averaging values via neural networks. prediction, modeling and analysis of complex systems. In: Advances in Intelligent Systems and Computing, vol. 289. Springer, Cham (2014)
Prem, V., Rao, K.U.: Time series decomposition model for accurate wind speed forecast, Renewables: Wind, Water and Solar, pp. 2–18 (2015)
Akpinar, M., Yumusak, N.: Year ahead demand forecast of city natural gas using seasonal time series methods. J. Energies 9(9), 727 (2016)
Fonseca-Delgado, R., Gómez-Gil, P.: Modeling diversity in ensembles for time-series prediction based on self-organizing maps. In: Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol. 428. Springer, Cham (2016)
Averkin, A., Yarushev, S., Dolgy, I., Sukhanov, A.: Time series forecasting based on hybrid neural networks and multiple regression. In: Advances in Intelligent Systems and Computing, vol 450. Springer, Cham (2016)
Gao, Y., Shang, H.L., Yang, Y.: High-dimensional functional time series forecasting. In: Functional Statistics and Related Fields. Contributions to Statistics. Springer, Cham (2017)
Takens, F.: Detecting strange attractors in fluid turbulence. Dynamical Systems and Turbulence. Springer, Heidelberg (1981)
Singh, M.K.: A new optimization method based on adaptive social behavior: ASBO. In: ASIC 174, 823–831. Springer, New Delhi (2012)
Acknowledgement
This research has done in Manuro Tech Research Pvt.Ltd., Bengaluru, India, under Innovative solution for Future Technology program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ashwini, N., Patil, M.R., Singh, M.K. (2018). Computational Intelligence Based Chaotic Time Series Prediction Using Evolved Neural Network. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_38
Download citation
DOI: https://doi.org/10.1007/978-981-10-7566-7_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7565-0
Online ISBN: 978-981-10-7566-7
eBook Packages: EngineeringEngineering (R0)