Abstract
A procedure based on the use of radial basis function network (RBFN) is presented for black box modeling of nonlinear dynamical systems. The generalization ability of RBFN is invoked to approximate the mathematical model of the given unknown nonlinear plant. This approximate model will then be used to predict the output of the plant at any given time instant. The parameters associated with RBFN are updated using the recursive equations obtained through the gradient-descent principle. The other benefit of using gradient descent principle is that it exhibits the clustering effect while adjusting the radial centers of RBFN. Real-time data of two benchmark problems: Box-Jenkins gas furnace data and Chemical process (polymer production), were used to show the application of RBFN for modeling purpose. Simulation results show that RBFN is well suited as a modeling tool for capturing the unknown nonlinear dynamics of the plant.
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
Behera, L., Kar, I.: Intelligent Systems and control principles and applications. Oxford University Press, Inc. (2010)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. John Wiley & Sons (2015)
Du, K.L., Swamy, M.: Radial basis function networks. In: Neural Networks and Statistical Learning, pp. 299–335. Springer (2014)
Esfe, M.H., Saedodin, S., Bahiraei, M., Toghraie, D., Mahian, O., Wongwises, S.: Thermal conductivity modeling of mgo/eg nanofluids using experimental data and artificial neural network. Journal of Thermal Analysis and Calorimetry 118(1), 287–294 (2014)
Kitayama, S., Huang, S., Yamazaki, K.: Optimization of variable blank holder force trajectory for springback reduction via sequential approximate optimization with radial basis function network. Structural and Multidisciplinary Optimization 47(2), 289–300 (2013)
Singh, M., Srivastava, S., Gupta, J., Handmandlu, M.: Identification and control of a nonlinear system using neural networks by extracting the system dynamics. IETE journal of research 53(1), 43–50 (2007)
Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.Y., Hjalmarsson, H., Juditsky, A.: Nonlinear black-box modeling in system identification: a unified overview. Automatica 31(12), 1691–1724 (1995)
Srivastava, S., Singh, M., Hanmandlu, M.: Control and identification of non-linear systems affected by noise using wavelet network. In: Computational intelligence and applications. pp. 51–56. Dynamic Publishers, Inc. (2002)
Srivastava, S., Singh, M., Hanmandlu, M., Jha, A.N.: New fuzzy wavelet neural networks for system identification and control. Applied Soft Computing 6(1), 1–17 (2005)
Srivastava, S., Singh, M., Madasu, V.K., Hanmandlu, M.: Choquet fuzzy integral based modeling of nonlinear system. Applied Soft Computing 8(2), 839–848 (2008)
Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on fuzzy systems 1(1), 7–31 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, R., Srivastava, S., Gupta, J.R.P. (2017). A Soft Computing Approach for Modeling of Nonlinear Dynamical Systems. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)