在战场等复杂环境得到的混合气体的红外光谱主次吸收峰交错重叠, 因此对其定性识别的特征提取方法就显得尤为重要。采集到的各种化学战剂和有机气体的红外光谱数据都是高维度数据, 首先采用中心化后降维进行特征提取来尽可能多地捕获到它所包含的本质信息, 由于混合气体的红外光谱是非线性、非高斯性信号, 把非高斯性作为独立性度量将各成分作为独立分量分离出来, 为了满足实时需求, 在传统快速独立成分分析(FastICA)算法的基础上对其迭代过程进行优化, 并应用极限学习机(ELM)建立模型进行定量分析。实验结果表明: 改进算法的迭代次数较传统算法减少, 定量分析均方差E=2.392 6×10-4, 回归系数R=0.999, 说明该方法在不影响分离精度的前提下提高了混合物质中纯物质光谱分离出来的效率。

The infrared spectrum of mixed gas got in the battlefield and complex environment results in overlapping and stagger of the primary and secondary peaks, so its feature extraction of qualitative recognition is particularly important. The infrared spectral data collected from a variety of chemical warfare agents and organic gases are high-dimensional data. Centralizing before reducing dimension was used for feature extraction to capture the essence of more information it contained. Since the infrared spectrum of the mixed gas was non-linear and non-Gaussian signal, this method regarded non-Gaussian as independence measure to separate each component as independent component. In order to meet real-time requirements, its iterative process was optimized based on the traditional fast independent component analysis(FastICA) algorithm and extreme learning machine(ELM) model was applied to quantitative analysis. Experiment results show that the iterations of optimized method reduces compared with the traditional method and mean square error of quantitative analysis is E=2.392 6×10-4 and regression coefficient is R=0.999. And the optimized method improves the isolated efficiency of separating pure substances spectra from mixture substances without affecting the separate accuracy.