基于核Fisher判别分析和遗传算法的混响环境下钴结壳识别方法

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

钴结壳采矿区底质的识别能够提高采矿效率。针对单一特征识别钴结壳采矿区底质效果较差的问题,提出了一种基于多特征集的钴结壳采矿区底质识别方法。该方法利用线性预测和小波分析提取了目标回波的线性预测系数和线性预测倒谱系数,分别得到了模极大值、子带能量和多分辨率奇异谱熵的特征矩阵。然后,用核Fisher判别分析(KFDA)对得到的特征进行压缩。最后,利用遗传算法(GA)对输出特征进行选择,得到最优特征子集,并将分类器的识别结果作为遗传适应度函数。该方法的优点是能够更全面地描述信号特征,选择有利特征,最大限度地去除冗余特征。实验结果证明,与单纯使用KFDA或GA相比,识别率得到了提高。

Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.