Name
Title | ||
|---|---|---|
Stability and convergence analysis of a neural model applied in nonlinear systems optimization |
Abstract | ||
|---|---|---|
A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-44989-2_24 | ICANN |
Keywords | Field | DocType |
equilibrium point,modified hopfield network,internal parameter,developed methodology,modified hopfield model,simulation result,nonlinear optimization problem,valid-subspace technique,optimal feasible solution,convergence analysis,neural model,nonlinear systems optimization,nonlinear optimization,nonlinear system,hopfield network | Convergence (routing),Mathematical optimization,Nonlinear system,Computer science,Nonlinear programming,Equilibrium point,Non linear model,Artificial intelligence,Artificial neural network,Hopfield network,Machine learning | Conference |
Volume | ISSN | ISBN |
2714 | 0302-9743 | 3-540-40408-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan Nunes da Silva | 1 | 176 | 52.11 |
| M. Okyay Kaynak | 2 | 2378 | 178.15 |
| Ethem Alpaydin | 3 | 858 | 90.05 |
| Erkki Oja | 4 | 6701 | 797.08 |
| lei xu | 5 | 0 | 0.34 |