A new optimization algorithm for single hidden layer feedforward neural networks

Applied Soft Computing Journal

Volumn 13, Issue 5, 2013, Pages 2857-2862

  Li, Leong Kwan ^a   Shao, Sally ^b   Yiu, Ka Fai Cedric ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b CLEVELAND STATE UNIVERSITY   (United States)

Author keywords

Evolutionary algorithm; Feedforward neural networks; Training of neural networks

Indexed keywords

COMPUTATION THEORY; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; NETWORK LAYERS; NONLINEAR PROGRAMMING; OPTIMIZATION; RADIAL BASIS FUNCTION NETWORKS;

COMPUTATIONAL EXPERIMENT; EXPLORATION AND EXPLOITATION; FUNCTION APPROXIMATION TECHNIQUES; GLOBAL OPTIMAL SOLUTIONS; NON-LINEAR OPTIMIZATION PROBLEMS; OPTIMIZATION ALGORITHMS; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS; UNIVERSAL APPROXIMATION;

FEEDFORWARD NEURAL NETWORKS;

EID: 84885640902     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2012.04.034     Document Type: Article

References (17)

1. G.J. Gibson, Exact classification with two-layer neural nets, Journal of Computer and System Sciences 52 (1996) 349-356. (Pubitemid 126375145)
2. K. Hornik, M. Stinchcombe, H. White, Multi-layer feedforward networks are universal approximators, Neural Networks 2 (1989) 359-366. (Pubitemid 20609008)
3. L.K. Li, On computing decision regions with neural nets, Journal of Computer and System Sciences 43 (1991) 509-512.
4. G.B. Huang, L. Chen, CK. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17 (4) (2006) 1420-1434.
5. DA Karras, S.J. Perantonis, An effective constrainted training algorithm for feedforward networks, IEEE Transactions on Neural Networks 6 (6) (1995) 879-892.
6. CK. Kwong, Y.K. Chan, M.E. Aydin, T.C Fogarty, An orthogonal array based genetic algorithm for developing neural network for fluid dispensing process, International Journal of Production Research 44 (12) (2006) 4815-4836. (Pubitemid 44536484)
7. CK. Kwong, Y. Chen, K.Y. Chan, H. Wong, Hybrid fuzzy least-squares regression approach to modeling manufacturing processes, IEEE Transactions on Fuzzy Systems 16 (3) (2008) 644-651.
8. G.P. Nicolas, O.B. Domingo, H.M. Cesar, An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization, Neural Networks 19 (4) (2006) 514-528.
9. S. Razavi, B. Tolson, A new formulation for feedforward neural networks, IEEE Transactions on Neural Networks 22 (10) (2011) 1588-1598.
10. J.J. Rubio, P. Angelov, J. Pacheco, Uniformly stable backpropagation algorithm to train a fedforward neural network, IEEE Transactions on Neural Networks 22 (3)(2011)356-366.
11. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representation by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), in: Parallel Distributed Processing, vol. 1, MIT Press, Cambridge, MA, 1986, pp. 318-362.
12. C.Y. Shen, L.X. Wang, Q. Li, Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method, Journal of Materials Processing Technology (2007) 183, p412V418.
13. C Sivapathasekaran, S. Mukherjee, A. Ray, A. Gupta, R. Sen, Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant, Bioresource Technology 101 (8) (2010)2884-2887.
14. J.T. Tsai, J.H. Chou, T.K. Liu, Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm, IEEE Transactions on Neural Networks 17 (1) (2006) 69-80.
15. D. Venkatesan, A.K. Kannan, A.R. Saravanan, A genetic algorithm-based artificial neural network model for the optimization of machining processes, Neural Computing & Applications 18 (2009) 135-140.
16. J.Y.F. Yam, T.W.S. Chow, A weight initialization method for improving training speed in feed forward neural network, Neurocomputing 30 (1-4) (2000) 219-232.
17. K.F.C Yiu, Y. Liu, K.L. Teo, A hybrid descent method for global optimization, Journal of Global Optimization 28 (2) (2004) 229-238.