Magnetic Optimization Algorithm for training Multi Layer Perceptron

2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011

Volumn , Issue , 2011, Pages 42-46

  Mirjalili, Seyedali ^a   Sadiq, Ali Safa ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Back Propagation algorithm; BP; Magnetic Optimization Algorithm; MLP; MOA; Multi layer perceptron

Indexed keywords

BP; MAGNETIC OPTIMIZATION ALGORITHM; MLP; MOA; MULTI LAYER PERCEPTRON;

APPROXIMATION ALGORITHMS; BACKPROPAGATION; CONVERGENCE OF NUMERICAL METHODS; FEEDFORWARD NEURAL NETWORKS; GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; MAGNETIC FIELDS; OPTIMIZATION; PROBLEM SOLVING;

LEARNING ALGORITHMS;

EID: 80053139335     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSN.2011.6014845     Document Type: Conference Paper

References (12)

1. B. Irie and S Miyake, "Capabilities of three-layered perceptrons, " in IEEE International Conference on Neural Networks, 1988, pp. 641-648.
2. D.S Chen and R.C.lain, "A robust Back-propagation Algorithm for Function Approximation, " IEEE Trans. Neural Network, pp. 467-479, 1994.
3. CH. Chen and H. Lai, "An Empirical Study Of The Gradient Descent And The Conjugate Gradient Backpropagation Neural Networks, " in Proceedings. OCEANS '92. 'Mastering the Oceans Through Technology'., 1992, pp. 132-135.
4. MT Hagan and M.B. Menhaj, "Training feedforward networks with the Marquardt algorithm, " Neural Networks. IEEE Transactions on, vol. 5, no. 6, pp. 989-993, 1994.
5. JR Zhang, .1 Zhang, TM Lock, and MR Lyu, "A hybrid particle swann optimization-back-propagation algorithm for feed forward neural network training, " ;J Applied Mathematics and Computation, vol. 128, pp. 1026-1037, 2007.
6. M. Gori and A. Tesi, "On the problem of local minima in backpropagation, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 76-86, 1992.
7. M.K Weirs, "A method for selt-detennination of adaptive learning rates in back propagation, " Neural Networks, vol. 4, pp. 371-379, 1991.
8. A van Ooyen and B Niehuis, "Improving the convergence of the back-propagation algorithm, " Neural Network, vol. 5, no. 3, pp. 465-471, 1992.
9. E.A. Grimaldi, F. Grimaccia, M. Mussetta, and R.E. Zich, "PSO as an etlective learning algorithm for neural network applications, " in 3rd International Conference on Computational Electromagnetics and Its Applications, 2004, pp. 557-560.
10. J Monta and Davis.L, "Training Feedforward Neural Networks Using Genetic Algorithms, " in Proceedings of the eleventh international joint, 1989.
11. Yi Da and Ge Xiurun, "An improved PSO-based ANN with simulated annealing technique, " Neurocomputing, vol. 63, pp. 527-533, 2005.
12. M.H. Tayarani-N and M.R Akbarzadeh-T, "Magnetic Optimization Algorithms a new synthesis, " in iEEE Congress on Evolutionary Computation, 2008, pp. 2659-2664.