A new Adaptive Immune Clonal Algorithm for Underwater Acoustic Target Sample Selection
IEEE TENCON 2013——The performance of underwater acoustic target classification decreases and is unstable when the training set contains noisy, redundant or irrelevant samples. In this talk, a new adaptive immune clonal sample selection algorithm (AICISA) is proposed to address this problem. AICISA is aimed at directing generation evolution. An experiment about the application of AICISA using the multi-field features extracted from 4 kinds of underwater acoustic targets was conducted. Experimental results show that AICISA can select effective subsets of samples. Reducing the sample size by 90%, the classification accuracy of SVM is improved by 10%. AICISA also shows good convergence and stability. The optimal subset of samples obtained by AICISA has good generalization ability and can remarkably reduce the classification time.
关键词： 水声目标 自适应免疫克隆样本选择算法 泛化能力 IEEE TENCON 2013
主讲人：Honghui Yang 机构：Northwestern Polytechnical University
时长：0:11:09 年代：2013年
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