Name
Abstract | ||
|---|---|---|
Aiming at the problem of parameter estimation in radar detection, a modified RBF neural network is proposed to estimate parameter accurately because of its good approximation ability to random nonlinear function and quick convergence speed. Two classical detection methods, which widely used in radar field, are listed in this paper, and their corresponding parameters are estimated with modified RBF neural network. Theoretical analysis and numerical results both show that the proposed method has good parameter estimation accuracy and quick convergence speed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_120 | ISNN (3) |
Keywords | Field | DocType |
parameter estimation | Radar,Convergence (routing),Radial basis function network,Radial basis function,Pattern recognition,Computer science,Hierarchical RBF,Probabilistic neural network,Artificial intelligence,Estimation theory,Artificial neural network,Machine learning | Conference |
Volume | Issue | ISSN |
4493 LNCS | PART 3 | 0302-9743 |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 1 | 1.71 |
| Xiaoyan Ma | 2 | 1 | 1.04 |
| Qianhong Lu | 3 | 1 | 0.36 |
| Bin Liu | 4 | 1 | 0.36 |
| Deng Bin | 5 | 1 | 0.36 |