Abstract
Rough set theory (RST) was introduced into radar emitter signal (RES) recognition. A novel approach was proposed to discretize continuous interval valued features and attribute reduction method was used to select the best feature subset from original feature set. Also, rough neural network (NN) classifier was designed. Experimental results show that the proposed hybrid approach based on RST and NN achieves very high recognition rate and good efficiency. It is proved to be a valid and practical approach.
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Zhang, G., Hu, L., Jin, W. (2004). Radar Emitter Signal Recognition Based on Feature Selection Algorithm. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_109
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DOI: https://doi.org/10.1007/978-3-540-30549-1_109
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