非线性荧光光谱的神经网络分析及其应用

大功率超快脉冲激光和气体相互作用可产生非线性荧光光谱，不同的气体分子具有不同的非线性荧光光谱。因而这种光谱可以作为物质的指纹模式加以识别分类，进而获知气体的成分。由于不同气体分子的光谱在同一波段上有很大的交叉重叠，用传统的光谱分析方法分析存在困难，采用神经网络方法分析上述非线性荧光光谱，利用经过预处理的荧光光谱数据作为模式样本，其中一部分样本作为学习样本对级联神经网络进行训练，用训练好的网络对所有样本进行实时识别，学习样本和测试样本的的正确识别率均可达100％，结果表明此方法可实时判断混合气体的组分。

Neural Network Analysis and Application of Nonlinear Fluorescence Spectra

Nonlinear fluorescence with distinguishable molecular spectra is emitted when fs laser pulses are launched in air due to the nonlinear effects between fs laser pulse and gases. Since every molecule has its particular feature in the fluorescence spectra, these fluorescence spectra can be used to analyze the components of gases in the air. However, since the spectra created by different molecule overlap, it is hard to analyze the nonlinear spectra by the conventional spectroscopic analysis methods. A cascaded neural network model is proposed to analyze the nonlinear fluorescence spectra. To improve learning speed of the neural network and the recognition rate, some preprocessing has been done. 100% correct recognition rates are achieved for both training spectrum samples and test spectrum samples. The simulations show that the proposed algorithm is a new effective method for real time recognizing the gas components without analytical sampling.