Name
Abstract | ||
|---|---|---|
This study evaluates the performance of the multilayer-perceptron and the frequency-sensitive competitive learning network in iden(cid:173) tifying five commercial aircraft from radar backscatter measure(cid:173) ments. The performance of the neural network classifiers is com(cid:173) pared with that of the nearest-neighbor and maximum-likelihood classifiers. Our results indicate that for this problem, the neural network classifiers are relatively insensitive to changes in the net(cid:173) work topology, and to the noise level in the training data. While, for this problem, the traditional algorithms outperform these sim(cid:173) ple neural classifiers, we feel that neural networks show the poten(cid:173) tial for improved performance. |
Year | Venue | Keywords |
|---|---|---|
1988 | Advances in neural information processing systems 1 | noisy radar signal,synthetic neural network classification,neural network |
Field | DocType | ISBN |
Competitive learning,Neural network classification,Radar signals,Pattern recognition,Computer science,Random subspace method,Probabilistic neural network,Network topology,Time delay neural network,Artificial intelligence,Artificial neural network,Machine learning | Conference | 1-558-60015-9 |
Citations | PageRank | References |
4 | 0.71 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. C. Ahalt | 1 | 114 | 13.12 |
| F. D. Garber | 2 | 28 | 8.48 |
| I. Jouny | 3 | 4 | 0.71 |
| A. K. Krishnamurthy | 4 | 61 | 7.75 |