Name
Title | ||
|---|---|---|
Self-Organizing RBF Neural Network Using an Adaptive Gradient Multiobjective Particle Swarm Optimization. |
Abstract | ||
|---|---|---|
One of the major obstacles in using radial basis function (RBF) neural networks is the convergence toward local minima instead of the global minima. For this reason, an adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm is designed to optimize both the structure and parameters of RBF neural networks in this paper. First, the AGMOPSO algorithm, based on a multiobjectiv... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCYB.2017.2764744 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Biological neural networks,Algorithm design and analysis,Convergence,Neurons,Gradient methods | Convergence (routing),Particle swarm optimization,Gradient method,Mathematical optimization,Radial basis function,Algorithm design,Maxima and minima,Multi-swarm optimization,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
49 | 1 | 2168-2267 |
Citations | PageRank | References |
6 | 0.40 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Gui Han | 1 | 476 | 39.06 |
| Xiao-Long Wu | 2 | 30 | 2.77 |
| Lu Zhang | 3 | 163 | 40.09 |
| Yu Tian | 4 | 49 | 19.62 |
| Jun-Fei Qiao | 5 | 798 | 74.56 |