针对已有光纤安防系统入侵行为识别模型中特征空间不完备及分类器泛化能力差的缺陷，本文提出了一种光纤入侵行为融合特征的集成识别策略。首先，采用总体经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、功率谱分析(Power Spectral Analysis, PSA)及离散小波变换(Discrete Wavelet Transform, DWT)提取信号在时域、频域及小波域内振动信息，构建入侵信号的特征集。然后，提出一种基于DFPA(Discriminative FunctionPruning Analysis, DFPA)的特征选取方法，实现特征空间的约简。最后，构建集成的随机权向量函数连接网络(Random Vector Functional-Link net, RVFL)分类器识别入侵行为。在基于M-Z (Mach-Zehnder, M-Z)干扰仪的光纤安防系统中采集入侵信号，进行实验，结果表明该策略的有效性。

For the incompletion of the eigenspace and the poor generalization ability of the pattern classifier in the past cognitive system, an ensemble cognitive method for intrusion behavior based on blending features is explored. Initially, use the Ensemble Empirical Mode Decomposition (EEMD), Power Spectral Analysis (PSA) and Discrete Wavelet Transform (DWT) to extract the information of the distribution tendency of the fiber optic signal on the time domain, frequency domain and the wavelet domain to build a relatively completed eigenspace of the fiber optic signal. And then use the Discriminative Function Pruning Analysis (DFPA) feature subset selection method to evaluate the ability of the feature element to discriminate different kinds of intrusion behavior, and then find the best feature subset. The simplification procedure for the feature group is thus accomplished. Lastly, use the ensemble modeling based on Random Vector Functional-Link net (RVFL) to improve the generalization ability of this cognitive model. Simulation experiment on fiber optic signal collected from the fiber optic perimeter security system based on M-Z(Mach-Zehnder, M-Z)interferometer has shown the effectiveness of this cognitive method.