摘要
提出利用物质的荧光光谱联合人工神经网络识别大气中杂质气体成分的新方法。物质的非线性荧光光谱与其分子原子结构有关,所以当大气中含有不同的杂质(有害)气体时,混合气体具有不同的非线性荧光光谱,通过对气体非线性荧光光谱的分析,可以确定大气中所含杂质气体的成分。掺杂气体的非线性荧光光谱是通过大功率超短激光脉冲与气体的非线性作用得到的;对非线性荧光光谱的识别则采用人工神经网络的方法。实验及计算机仿真模拟结果表明,这是一个确实可行的识别大气中杂质气体成分的新方法。
A new method for recognizing gas components in air based on nonlinear fluorescence spectra combined with a neural network model was proposed.The nonlinear fluorescence spectra are related to the molecular structure of material.The nonlinear fluorescence spectra,therefore,will change when an impure gas appears in the air so that they can be used to analyze the impure gas components in the air.We obtained the nonlinear fluorescence spectra by nonlinear action between the fs laser pulse and the impure air.A WTA neural network model was employed in order to faster recognize the gas components because of a lot of fluorescence spectra and across spectra data among different gases.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2003年第9期954-957,共4页
Journal of Optoelectronics·Laser
基金
国家自然科学基金资助项目(60277022)
天津市自然科学基金重点资助项目(023800811)
关键词
气体识别
人工神经网络
非线性荧光光谱
飞秒激光脉冲
neural network
gas recognition
nonlinear fluorescence spectra of gases
fs laser pulse
