Name
Abstract | ||
|---|---|---|
In this paper, a self-organizing radial basis function (SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2016.2616413 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Algorithm design and analysis,Neurons,Biological neural networks,Training,Convergence,Optimization | Particle swarm optimization,Mathematical optimization,Algorithm design,Radial basis function,Computer science,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Artificial intelligence,Artificial neural network,Machine learning,Network model | Journal |
Volume | Issue | ISSN |
29 | 1 | 2162-237X |
Citations | PageRank | References |
12 | 0.50 | 52 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Gui Han | 1 | 476 | 39.06 |
| Wei Lu | 2 | 319 | 62.97 |
| Ying Hou | 3 | 40 | 3.43 |
| Jun-Fei Qiao | 4 | 798 | 74.56 |