基于PSO-GWO-SVM的周界安防信号识别研究

针对支持向量机(SVM)在大规模入侵信号分类时存在的局限性,提出了一种改进的SVM信号识别方法。该方法首先采用粒子群优化算法(PSO)来生成多样化的初始位置,然后利用灰狼优化算法(GWO)更新离散搜索空间中样本的当前位置,获得最优特征子集；最后基于最优特征子集用SVM对待测样本进行分类识别。实验结果显明,在识别周界入侵信号时,基于PSO-GWO-SVM算法的分类器获得了96.86%的准确率、95.82%的灵敏度(SE)和96.31%的特异性。与传统的信号识别方法相比,具有更优异的识别精度、适应性和时效性。

Study of perimeter security signal recognition based on PSO-GWO-SVM

Aiming at the limitation of support vector machine (SVM) in the classification of large-scale intrusion signals,an improved SVM signal recognition method is proposed.Firstly,the particle swarm optimization algorithm (PSO) is used to generate the initial position with diversity.Then,the current position of the sample in the discrete search space is updated by the gray wolf optimization algorithm (GWO),and the optimal feature subset is obtained.Finally,the SVM is used to classify and recognize the samples to be tested based on the optimal feature subset.The experimental results show that the classifier based on PSO-GWO-SVM algorithm achieves the accuracy of 96.86%,sensitivity (SE) of 95.82% and specificity of 96.31%.Compared with the traditional signal recognition method,it has better recognition precision,adaptability and timeliness.