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中文核心期刊

具有高泛化性能的无源声呐目标识别算法

Target recognition algorithm for passive sonar system with high Generalization Ability

  • 摘要: 针对水声信号的特性和无源声呐目标识别的特点,提出了一种有师自组织神经网络分类算法。该算法主要针对水声信号的样本不完备的问题,在前债神经网络中引入了多重神经元激活模型和自组织竞争学习算法,使无源声呐分类系统的泛化性能有了明显的提高。该算法采用分层学习策略,有效地节省了训练时间,同时减少了陷入局部最优解的概率。通过对实录海上无源声呐目标信号的分类实验,检验了算法的识别能力和泛化能力,实验结果责明该算法具有良好的泛化能力同时保持了较高的识别率.

     

    Abstract: In this paper,a new algorithm based on a supervised selforganizing neural network for the passive sonar target recognition was proposed.Because of the incompletement of the passive sonar sampling pattern set,this algorithm introduced a mult i-active-function struct ure and self-organizing competitive learning algorithm into the classic feed-forward neural network,and obviously improved the generalization ability of target recognition.Besides,it can efficiently reduce the learning time andavoided the local optimum.The recognition experiments of real passive sonar signals say that this new algorithm has good generalization ability and high recognition rate.

     

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