A Hybrid Neural Network and Gravitational Search Algorithm (HNNGSA) Method to Solve well known Wessinger's Equation

World Academy of Science, Engineering and Technology

Volumn 73, Issue , 2011, Pages 803-807

  Ghalambaz, M ^a   Noghrehabadi, A R ^a   Behrang, M A ^b   Assareh, E ^b   Ghanbarzadeh, A ^a   Hedayat, N ^b  

^a SHAHID CHAMRAN UNIVERSITY OF AHVAZ   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Gravitational Search Algorithm (GSR); Neural Networks; Wessinger's Equation

Indexed keywords

ADJUSTABLE PARAMETERS; ANALYTIC SOLUTION; APPROXIMATION SOLUTION; HYBRID NEURAL NETWORKS; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; SEARCH ALGORITHMS; WESSINGER'S EQUATION;

APPROXIMATION ALGORITHMS; BOUNDARY CONDITIONS; LEARNING ALGORITHMS; NETWORK LAYERS; NUMERICAL METHODS;

NEURAL NETWORKS;

EID: 79953016203     PISSN: 2010376X     EISSN: 20103778     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. J.A. Khan, R.M.A. Zahoor, I.M. Qureshi, Swarm intelligence for the problem of non-linear ordinary differential equations and its application to well known Wessinger's equation. European Journal of scientific research. 2009; 34(4): 514-525.
2. I.E. Lagris, A. Likas, D.I. Fotiadis. Artificial neural networks for solving ordinary and partitial differential equations. IEEE Transactions on Neural Networks. 1998; 9 (5): 987-1000.
3. D. Gottlieb, S.A. Orszag, Numerical analysis of spectral methods: theory and applications, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 26, SIAM, Philadelphia, 1977.
4. H. Lee, I.S. Kang, Neural algorithms for solving differential equations, Journal of Computational Physics 1990; 91: 110-131.
5. A.J. Meade Jr, A.A. Fernandez, The numerical solution of linear ordinary differential equations by feedforward neural networks, Mathematical and Computer Modelling. 1994; 19 (12): 1-25.
6. A. Malek, R.S. Beidokhti, Numerical solution for high order differential equations using a hybrid neural network-Optimization method. Applied Mathematics and Computation. 2006; 183: 260-271.
7. D.T. Pham, E. Koc, A. Ghanbarzadeh, S. Otri. Optimisation of the weights of multi-layered perceptrons using the bees algorithm. Proceedings of 5th International Symposium on Intelligent Manufacturing Systems. Sakarya University, Department of Industrial Engineering, 2006; pp. 38-46, May 29-31.
8. A.S. Yilmaz, Z. Ozer, Pitch angle control in wind turbines above the rated wind speed by multi-layer percepteron and Radial basis function neural networks. Expert Systems with Applications. 2009; 36: 9767-9775.
9. D.T. Pham, X. Liu, Neural Networks for Identification, Prediction and Control. Springer Verlag, London. 1995.
10. R.P. Lippmann, An introduction to computing with neural nets, IEEE ASSP Magazine 1987; 4-22.
11. K. Hornick, M. Stinchcombe, H. white, Multilayer feedforward networks are universal approximators, Neural Networks 1989; 2 (5): 359-366.
12. M.A. Behrang, E. Assareh, A. Ghanbarzadeh, A.R. Noghrehabadi, The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy 2010; 84: 1468-1480.
13. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A Gravitational Search Algorithm. Information Sciences. 2009; 179: 2232-2248.
14. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, Filter modeling using gravitational search algorithm. Energy policy; doi:10.1016/j.engappai.2010.05.007.