Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30576
Identification of Nonlinear Systems Using Radial Basis Function Neural Network

Authors: C. Pislaru, A. Shebani

Abstract:

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: System Identification, Nonlinear System, RBF neural network, Neural networks

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097183

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2398

References:


[1] S. Haykin, “Neural Networks, A Comprehensive Foundation”, Prentice- Hall Inc., second edition, USA, 1999.
[2] Bose, and P. Liang, “Neural Network Fundamentals with Graphs, Algorithms and Applications”, McGraw-Hill series in Electrical and Computer Engineering, USA, 1996.
[3] Kriesel, “A Brief Introduction to Neural Networks”, Zeta2, University of Bonn, Germany, 2005.
[4] J. Li, and F. Zhao, “Identification of Dynamical Systems Using Radial Basis Function Neural Networks with Hybrid Learning Algorithm”, Systems and Control in Aerospace and Astronautics, ISSCAA 2006.
[5] M. Y. Mashor, “Nonlinear system identification using RBF networks with linear input connections”, Malaysian Journal of Computer Science, Vol. 11 No. 1, June 1998, pp. 74-80.
[6] M. Jafari, T. Alizadeh, M. Gholami, A. Alizadeh and K. Salahshoor “On-line Identification of Non-Linear Systems Using an Adaptive RBFBased Neural Network”, Proceedings of the World Congress on Engineering and Computer Science WCECS 2007, San Francisco, USA.
[7] A. Zahir and C. Abdelfettah , “Nonlinear Systems Modelling Using RBF Neural Networks A Random Learning Approach to the Resource Allocating Network Algorithm”, Proceedings of the 10th Mediterranean Conference on Control and Automation - MED2002, Portugal,
[8] M. Wilamowski, “Neural Network Architectures and Learning Algorithms: How Not to Be Frustrated with Neural Networks”, IEEE Industrial Electronics Magazine, vol. 3, pp. 56-63, Dec. 2009.
[9] B. M. Wilamowski and H. Yu, “Neural Network Learning without Backpropagation”, IEEE Trans. On Neural Networks, vol. 21, pp. 1793- 1803, Nov. 2010.
[10] V. Mladenov, P. Koprinkova-Hristova, G. Palm, A. Villa, B. Apolloni, and K. Kasabov, “Artificial Neural Networks and Machine Learning”, Springer, USA, 2013.
[11] L. D. Kiernan, J. D. Maso, and K. Warwick, “Robust Initialization of Gaussian Radial Basis Function Networks Using Partitioned k-means Clustering”, IEE Electronic Letters, Vol. 32, No. 7, pp.671-673, 1996.
[12] L. Jinkun, “Radial Basis Function (RBF) Neural Network control for Mechanical Systems”, Springer, USA, 2013.
[13] Y. Hu and J. Hwang, “Hand book of Neural Network Signal Processing”, by CRC press LLC, USA, 2002.
[14] M. T. Hagan, M. B. Menhaj, “Training Feedforward Networks with the Marquardt Algorithm”, IEEE Trans. On Neural Networks, vol. 5, no. 6, pp. 989-993, Nov. 1994.
[15] N. B. Karayiannis, “Reformulated Radial Basis Neural Networks Trained by Gradient Descent”, IEEE Trans. Neural Networks, vol. 10, pp. 657-671, Aug. 2002.
[16] J. Fathala, “Analysis and implementation of radial basis function neural network for controlling non-linear dynamical systems”, PhD. Thesis, University of Newcastle Upon Tyne, UK. Department of Electrical and Electronic Engineering, 1998.
[17] J. Moody, and C. Darken, “Fast learning in Networks of Locally-Tuned Processing Units”, neural computation, Vol. 1, 1989.
[18] R. Mammone, “Artificial Neural Networks for Speech and Vision”, New Jersey, USA, 1994.
[19] L. Fu, “Neural Networks in Computer Intelligence”, university of Florida, 1994.
[20] Czarkowski, “Identification and optimization of PID parameters using Matlab”, Cork institute of technology, Cork, Poland, 2002.
[21] A. Abdulaziz, “Neural Based Controller Development for solving Nonlinear Control Problem”, PhD. Thesis, University of Newcastle Upon Tyne, UK, 1994.
[22] T. Phung, and V. Tzouansas, “Design and control of a twin tank water process”, Engineering department, University of Houston, Downtown, USA, 2012.
[23] Vasitstha, “PID Output Fuzzified Water Level Control in MIMO Coupled Tank System", International Journal of Mechanical Engineering and Technology (IJMET), VOL. 4, PP. 138-153, 2013.
[24] Laubwald, “Coupled tank system 1”, UK, 2014. www.control-systemspriciples. co.uk (online).
[25] J. Choi, “Control systems, Modelling of DC motors”, university of British Columbia, Canada, 2008.