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The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers

Year 2015, Volume: 3 , 252 - 255, 30.12.2015

Abstract

In this paper, Shimizu-Morioka Chaotic System (SMCS) is modelled using Feed Forward Artificial Neural Network. In the realized network model, Log-Sigmoid and Purelin transfer functions have been used for hidden and output layer, respectively. 3-10-3 network structure is created using MATLAB. The model inputs are the state variables of SMCS. Outputs represent not only the outputs of SMCS but also iterative versions of these inputs. For the equations’ numeric solutions of describing SMCS, Runge Kutta 5 Butcher (RK-5-B) algorithm which is one of the differential equation solution methods, is used. Samples in the structure of described network, the created different numbers of samples using RK-5-B have been used as input data and performance analysis have been performed for these data. As a result, the paper shows that when the sample data numbers increase, network modeling performance gives more successful results.

References

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Year 2015, Volume: 3 , 252 - 255, 30.12.2015

Abstract

References

  • [1] R. Adhikari, “A neural network based linear ensemble framework for time series forecasting”, Neurocomputing, Elsevier, vol. 157, pp. 231–242, 2015. [2] C. U. Vila, A. C. Z. Souza, J. W. M. Lima, and P. P. Balestrassi, “Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model”, Electrical Power and Energy Systems, vol. 32, pp. 108–116, 2010. [3] Q. Nie, L. Jin, S. Fei, and J. Ma, “Neural network for multiclassclassification by boosting composite stumps”, Neurocomputing, vol. 149, pp. 949–956, 2015. [4] A. J. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, and F. Jager, “Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women”, Neurocomputing, vol. 151, pp. 963–974, 2015. [5] V. Kumar, P. Gaur, and A. P. Mittal, “ANN based self tuned PID like adaptive controller design for high performance PMSM position control”, Expert Systems with Applications, vol. 41, pp. 7995–8002, 2014. [6] M. M. Noel, and B. J. Pandian, “Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach”, Applied Soft Computing, vol. 23, pp. 444–451, 2014. [7] G. Das, P. K. Pattnaik, and S. K. Padhy, “Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization”, Expert Systems with Applications, vol. 41, pp. 3491–3496, 2014. [8] J. D. Pintér, “Calibrating artificial neural networks by global optimization”, Expert Systems with Applications, vol. 39, pp. 25–32, 2012. [9] R. Archana, A. Unnikrishnan, and R. Gopikakumari, “An Improved EKF based neural network training algorithm for the identification of chaotic systems driven by time series”, 2012 IEEE International Conference on Power, Signals, Controls and Computation (EPSCICON), 2012. [10] H. Altınkaya, İ. M. Orak, and İ. Esen, “Artificial neural network application for modeling the rail rolling process”, Expert Systems with Applications, vol. 41, pp. 7135–7146, 2014. [11] M. M. El-Dessoky, M. T. Yassen, and E. S. Aly, “Bifurcation analysis and chaos control in Shimizu–Morioka chaotic system with delayed feedback, Applied Mathematics and Computation”, vol. 243, pp. 283–297, 2014 [12] X. Liao, F. Xu, P. Wang, and P. Yu, “Chaos control and synchronization for a special generalized Lorenz canonical system – The SM system”, Chaos, Solitons and Fractals, vol. 39, pp. 2491–2508, 2009. [13] S. Yu, W. K. S. Tang, J. Lü, and G. Chen, “Generation of nxm-Wing Lorenz-Like Attractors From a Modified Shimizu–Morioka Model”, IEEE Transactıons On Circuits And Systems—II: Express Briefs, vol. 55(11), 2008. [14] C. Tsitouras, Runge–Kutta pairs of order 5(4) satisfying only the first column simplifying assumption, Computers and Mathematics with Applications, vol. 62, pp. 770–775, 2011. [15] M. Tuna, C. B. Fidan, S. Kocabey, and S. Görgülü, “Effective and Reliable Speed Control of Permanent Magnet DC (PMDC) Motor under Variable Loads”, Journal of Electrical Engineering & Technology, vol. 10(5), pp. 2170–2178, 2015. [16] M. H. Beale, M. T. Hagan, and H. B. Demuth, “The Neural Network Toolbox User’s Guide R2014b”, The MathWorks, 2015. [17] Mohammed, E.Z., and Ali, H.K. “Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Array”, International Journal of Computer Theory and Engineering, 5(5), 780-783, 2013. [18] Sahin, I., and I. Koyuncu. “Design and Implementation of Neural Networks Neurons with RadBas, LogSig, and TanSig Activation Functions on FPGA”, Electronics and Electrical Engineering, vol. 120(4), pp. 51-54, 2012.
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Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Articlessi
Authors

Murat Alcın This is me

İhsan Pehlıvan

İsmail Koyuncu

Publication Date December 30, 2015
Published in Issue Year 2015 Volume: 3

Cite

APA Alcın, M., Pehlıvan, İ., & Koyuncu, İ. (2015). The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers. Balkan Journal of Electrical and Computer Engineering, 3, 252-255.

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