本文選題：粉塵靜電信號 + 新LMS算法��； 參考：《山東科技大學(xué)》2017年碩士論文

【摘要】：本論文針對傳統(tǒng)粉塵檢測中存在的問題,提出一種基于神經(jīng)網(wǎng)絡(luò)軟測量的粉塵濃度檢測新方法,對粉塵的研究具有重要的現(xiàn)實意義。首先,設(shè)計了一種新的變步長LMS算法來修改濾波器系數(shù),更好地提高了低信噪比下算法的收斂性和穩(wěn)態(tài)性能,實驗和仿真結(jié)果證明該算法能很好地濾除靜電信號中的隨機(jī)噪聲。其次,對濾波后的靜電信號進(jìn)行時頻分析,提出一種改進(jìn)相似極值的EEMD信號特征提取方法,并和EMD算法進(jìn)行了仿真對比,通過靜電信號的能量和能量熵值分析出靜電信號的變化和粉塵濃度的變化趨勢具有正相關(guān)性。然后,分別建立了 BP和RBF兩種神經(jīng)網(wǎng)絡(luò)粉塵濃度軟測量模型,根據(jù)實驗和經(jīng)驗相結(jié)合的方法確定了兩種模型的參數(shù),構(gòu)建出綜合性能最優(yōu)的模型。并通過分析比較和實驗驗證了 BP網(wǎng)絡(luò)模型在訓(xùn)練精度與泛化能力方面略優(yōu)于RBF網(wǎng)絡(luò)模型。最后,采用改進(jìn)遺傳算子的遺傳算法對BP神經(jīng)網(wǎng)絡(luò)粉塵濃度軟測量模型進(jìn)行優(yōu)化和改進(jìn)。實驗和仿真結(jié)果證明模型優(yōu)化后其收斂性能、運行時間和均方誤差均得到了改善,提高了模型的精確率和效率,證明了基于神經(jīng)網(wǎng)絡(luò)的軟測量模型對粉塵濃度進(jìn)行監(jiān)測的可行性。
[Abstract]:Aiming at the problems in traditional dust detection, a new method of dust concentration detection based on neural network soft sensing is proposed in this paper, which is of great practical significance to the study of dust. Firstly, a new variable step size LMS algorithm is designed to modify the filter coefficients, which improves the convergence and steady-state performance of the algorithm under low SNR. The experimental and simulation results show that the algorithm can effectively filter the random noise in electrostatic signals. Secondly, the time-frequency analysis of the filtered electrostatic signal is carried out, and an improved EEMD signal feature extraction method with similar extremum is proposed and compared with the EMD algorithm. By analyzing the energy and energy entropy of electrostatic signal, it is found that there is a positive correlation between the change of electrostatic signal and the change trend of dust concentration. Then, two kinds of BP and RBF neural network soft sensor models of dust concentration are established, and the parameters of the two models are determined according to the method of combining experiment and experience, and the optimal comprehensive performance model is constructed. The BP neural network model is better than the RBF network model in training accuracy and generalization ability. Finally, the improved genetic operator genetic algorithm is used to optimize and improve the BP neural network soft sensor model of dust concentration. Experimental and simulation results show that the convergence performance, running time and mean square error of the model are improved, and the accuracy and efficiency of the model are improved. The feasibility of monitoring dust concentration by soft sensor model based on neural network is proved.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;TP274

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本文編號：1989633