A novel hybrid model for inversion problem of atmospheric refractivity estimation
| Autor: | Cemil Tepecik, Isa Navruz |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
education.field_of_study
010504 meteorology & atmospheric sciences Artificial neural network Computer science Computer Science::Neural and Evolutionary Computation Population 020206 networking & telecommunications Inversion (meteorology) 02 engineering and technology Communications system 01 natural sciences Radio propagation 0202 electrical engineering electronic engineering information engineering Clutter Electrical and Electronic Engineering Propagation factor education Hybrid model Algorithm 0105 earth and related environmental sciences |
| Zdroj: | AEU - International Journal of Electronics and Communications. 84:258-264 |
| ISSN: | 1434-8411 |
| DOI: | 10.1016/j.aeue.2017.12.009 |
| Popis: | Atmospheric refractivity estimation is an important issue for performance evaluation of communication systems and air surveillance radars. A novel hybrid model based on artificial neural networks (ANNs) and genetic algorithms (GAs) for inversion problem of atmospheric refractivity estimation is introduced. In this paper, inversion problem and clutter model problem of refractivity from clutter (RFC) method are separated and only inversion problem is studied. A problem specific ANN structure is designed and an original GA is developed to fulfill atmospheric refractivity estimations. In hybrid method, ANNs make pre-estimation and GAs use these results as a starting population for post-estimation. When the results obtained from the single solutions of ANNs and GAs are compared to the results obtained from hybrid model, a significant improvement in the accuracy of estimated results is observed. |
| Databáze: | OpenAIRE |
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