本文選題：分形　切入點(diǎn)：小波　出處：《光譜學(xué)與光譜分析》2017年12期

【摘要】：由于光譜譜線存在自然展寬、多普勒展寬、碰撞展寬等,使混合氣體中多種成分的吸收光譜信號(hào)出現(xiàn)相鄰譜峰重疊現(xiàn)象,給混合氣體組成成分的定性或定量檢測(cè)帶來(lái)較大的困難�，F(xiàn)有的方法在獲取先驗(yàn)知識(shí)、處理精度、運(yùn)算效率等方面存在不足。提出基于時(shí)頻域分形維數(shù)分析的光譜信號(hào)重疊峰解析算法,結(jié)合小波的多尺度觀測(cè)能力和分形的自相似度的度量能力,識(shí)別、定位和解析光譜信號(hào)中的重疊峰。首先利用小波對(duì)具有重疊譜峰的光譜信號(hào)進(jìn)行光譜頻率域和尺度域的分析,然后對(duì)該時(shí)頻域的光譜信號(hào)在同一光譜頻率下的多尺度數(shù)據(jù)進(jìn)行自相似性度量和分形計(jì)算。逐頻率計(jì)算后得到光譜信號(hào)在頻率域的分形維數(shù)曲線。該曲線體現(xiàn)了光譜信號(hào)在不同尺度的自相似性,其極值位置與光譜信號(hào)的各獨(dú)立峰的位置具有相關(guān)性。依據(jù)此特性,結(jié)合分形曲線的特征參數(shù),最后利用神經(jīng)網(wǎng)絡(luò)解析出對(duì)應(yīng)混合氣體成分的混疊在一起的各個(gè)獨(dú)立譜峰。該方法利用小波的多分辨率特性,對(duì)信號(hào)進(jìn)行不同尺度的精細(xì)度量。分形模型則提高了系統(tǒng)解析復(fù)雜信號(hào)的能力,對(duì)重疊程度高的多譜峰重疊信號(hào)也有很強(qiáng)的處理能力。借助人工神經(jīng)網(wǎng)絡(luò),實(shí)現(xiàn)了整個(gè)算法的自動(dòng)測(cè)量。通過(guò)實(shí)驗(yàn)結(jié)果分析,驗(yàn)證了算法的有效性,并討論影響算法效果的主要因素。
[Abstract]:Due to the natural broadening of spectral lines, Doppler broadening, collision broadening and so on, the absorption spectrum signals of various components in the mixed gases overlap with each other. It brings great difficulties to the qualitative or quantitative detection of the composition of mixed gases. The existing methods are used to obtain prior knowledge and deal with the accuracy. The algorithm based on fractal dimension analysis in time-frequency domain is proposed to analyze the overlapped peaks of spectral signals, which combines the multi-scale observation ability of wavelet and the measurement ability of fractal self-similarity. The overlapping peaks in the spectral signals are located and analyzed. Firstly, the spectral signals with overlapping spectral peaks are analyzed in the spectral frequency domain and the scale domain by wavelet transform. Then self-similarity measurement and fractal calculation of the multi-scale data of the spectral signal in the same spectral frequency are carried out. The fractal dimension curve of the spectral signal in the frequency domain is obtained by frequency calculation. The self-similarity of spectral signals at different scales, The position of the extreme value is correlated with the position of each independent peak of the spectral signal. According to this characteristic, the characteristic parameters of the fractal curve are combined. Finally, the neural network is used to analyze the independent spectral peaks corresponding to the mixed gas components. The method utilizes the multi-resolution characteristic of wavelet. The fractal model improves the system's ability to analyze complex signals, and it also has a strong ability to deal with multi-spectral peaks overlapping signals with high overlap degree. Through the analysis of experimental results, the validity of the algorithm is verified, and the main factors affecting the effect of the algorithm are discussed.
【作者單位】： 武漢大學(xué)電子信息學(xué)院;電網(wǎng)環(huán)境保護(hù)國(guó)家重點(diǎn)實(shí)驗(yàn)室中國(guó)電力科學(xué)研究院;
【基金】：國(guó)家科技支撐計(jì)劃課題(2011BAF02B02)資助
【分類號(hào)】：TN911.74

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