Evolving Radial Basis Function Neural Network with Hausdorff Similarity Measure for SONAR signals detection/ classification
| Autor: | H. Peyvandi |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Engineering
Artificial neural network business.industry Hausdorff space Pattern recognition Similarity measure Sonar signal processing Measure (mathematics) Sonar Computer Science::Robotics ComputingMethodologies_PATTERNRECOGNITION Kernel (statistics) Artificial intelligence Hidden Markov model business |
| Zdroj: | OCEANS 2009-EUROPE. |
| DOI: | 10.1109/oceanse.2009.5278146 |
| Popis: | In this paper, a new approach has been proposed for detection/ classification of SONAR signals based on Radial Basis Function Neural Network (RBFNN), which has been modified with a robust and reliable measure named: Hausdorff Similarity Measure (HSM). Methodologies of approach and simulation results are also represented. The final results show the new approach is able to increase the total performance of detection/ classification of SONAR targets even in low SNR. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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