基于GA-LSSVR的煤礦瓦斯數(shù)據(jù)去噪研究
發(fā)布時(shí)間：2018-04-08 09:50
本文選題：瓦斯?jié)舛?/strong> 切入點(diǎn)：數(shù)據(jù)去噪 出處：《礦業(yè)安全與環(huán)保》2017年01期
【摘要】：針對(duì)煤礦瓦斯數(shù)據(jù)普遍含有噪聲的問(wèn)題,提出一種基于遺傳算法優(yōu)化的最小二乘支持向量回歸機(jī)(GA-LSSVR)的數(shù)據(jù)去噪算法。LSSVR通過(guò)求解只含一個(gè)等式約束的二次規(guī)劃問(wèn)題來(lái)求得最優(yōu)解,從而改進(jìn)了小波去噪局部最優(yōu)的缺點(diǎn)。但LSSVR也存在收斂速度慢的缺點(diǎn),通過(guò)遺傳算法(GA)優(yōu)化LSSVR,以提高算法的收斂速度。首先,對(duì)某煤礦的瓦斯?jié)舛葧r(shí)間序列進(jìn)行異常數(shù)據(jù)和缺失數(shù)據(jù)的處理,然后用GA-LSSVR建模訓(xùn)練。仿真實(shí)驗(yàn)結(jié)果表明,與小波去噪方法相比,GA-LSSVR能有效去除噪聲,并且能夠避免數(shù)據(jù)失真,把有效信號(hào)分離出來(lái),經(jīng)過(guò)計(jì)算,GA-LSSVR能將輸入輸出均方根誤差降低0.002 94,相對(duì)降低了34.59%,去噪效果較好;與LSSVR方法相比,GA-LSSVR能明顯縮短程序運(yùn)行時(shí)間,可提高運(yùn)行效率。
[Abstract]:Aiming at the problem that coal mine gas data generally contain noise, a data denoising algorithm named GA-LSSVRbased on genetic algorithm optimization is proposed. LSSVR solves the quadratic programming problem with only one equality constraint to obtain the optimal solution.Thus, the shortcomings of local optimal wavelet denoising are improved.However, LSSVR also has the disadvantage of slow convergence speed. Genetic algorithm (GA) is used to optimize LSS VRR to improve the convergence speed of the algorithm.Firstly, the abnormal data and missing data are processed in the time series of gas concentration in a coal mine, and then the GA-LSSVR modeling training is used.The simulation results show that GA-LSSVR can effectively remove noise, avoid data distortion and separate the effective signal compared with wavelet denoising method.GA-LSSVR can reduce the root mean square error (RMS) of input and output by 0.002, and reduce the RMS error by 34.59. Compared with the LSSVR method, GA-LSSVR can significantly shorten the running time and improve the running efficiency.
【作者單位】： 西安科技大學(xué)電氣與控制工程學(xué)院;河南科技學(xué)院信息工程學(xué)院;
【分類號(hào)】：TD712
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