Math. Model. Nat. Phenom.
Volume 5, Number 7, 2010JANO9 – The 9th International Conference on Numerical Analysis and Optimization
|Page(s)||132 - 138|
|Published online||26 August 2010|
- Q. Duan, S. Sorooshian, V. Gupta. Effective and efficient global optimization for conceptual rainfall runoff models. Water Resour. Res, 28 (1992), 1015-1031. [Google Scholar]
- R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory Proc. of 6th Symp. on micro machine and human science, IEEE service center, Piscataway, N.J., (1995), 39-43. [Google Scholar]
- K.L. Hsu, H.V. Gupta, S. Sorooshian. Artificial neural network modeling of the rainfall-rainoff process. Water Resour. Res., 31 (1995), No. 10, 2517-2530. [CrossRef] [Google Scholar]
- V. Maniezzo. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transaction on Neural Networks, 5 (1994), 39–53. [CrossRef] [Google Scholar]
- K.E. Parsopoulos, M.N. Vrahatis. Recent approaches to global optimization problems through particle swarm optimization. Natural Comput., 1 (2002), No. 23, 235-306. [CrossRef] [Google Scholar]
- D.E. Rumelhart, G.E. Hinton, R.J. Williams. Learning internal representation by error propagation. In: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Cambridge, MA, (1986), 318-362. [Google Scholar]
- A. Salman, I. Ahmad, S. Al-Madani. Particle swarm optimization for task assignment problem. Microproc. and Microsyst., 26 (2002), No. 8, 363-371. [CrossRef] [Google Scholar]
- R. S. Sexton, R. E. Dorsey, J. D. Johnson. Toward global optimization of neural networks: A comparison of the genetic algorithm and back propagation. Decision Support Systems, 22 (1998), 171–185. [CrossRef] [Google Scholar]
- J.M. Yang, C.Y. Kao. A robust evolutionary algorithm for training neural networks. Neural Comput. Appl., 10 (2001), 214–230. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.