【摘要】：雙穩(wěn)隨機(jī)共振是一種非線性現(xiàn)象，它只有在噪聲、雙穩(wěn)系統(tǒng)和輸入信號(hào)三者之間達(dá)到一定的協(xié)同關(guān)系時(shí)才會(huì)發(fā)生。具體針對(duì)某一確定的大參數(shù)含噪信號(hào)，需要對(duì)隨機(jī)共振的多個(gè)參數(shù)進(jìn)行聯(lián)合調(diào)節(jié)，才能產(chǎn)生隨機(jī)共振。但是，目前關(guān)于系統(tǒng)參數(shù)、信號(hào)頻率以及噪聲強(qiáng)度三者之間的耦合性的研究很少，隨機(jī)共振的聯(lián)合調(diào)參缺乏指導(dǎo)，只能依賴于人為經(jīng)驗(yàn)。因此，本文將圍繞隨機(jī)共振多參數(shù)聯(lián)合調(diào)節(jié)實(shí)現(xiàn)困難的問題，對(duì)自適應(yīng)隨機(jī)共振展開研究。 針對(duì)隨機(jī)共振系統(tǒng)參數(shù)選取依賴于人為經(jīng)驗(yàn)和變步長(zhǎng)隨機(jī)共振計(jì)算步長(zhǎng)選取困難的缺陷，提出一種采用粒子群算法的自適應(yīng)變步長(zhǎng)隨機(jī)共振方法，該方法以隨機(jī)共振輸出信噪比作為粒子群算法的適應(yīng)度函數(shù)，通過對(duì)系統(tǒng)參數(shù)和計(jì)算步長(zhǎng)的同步優(yōu)化，實(shí)現(xiàn)變步長(zhǎng)隨機(jī)共振最優(yōu)輸出的自適應(yīng)求解。利用仿真數(shù)據(jù)和工程實(shí)際數(shù)據(jù)對(duì)該方法進(jìn)行實(shí)驗(yàn)驗(yàn)證，分析結(jié)果表明該方法簡(jiǎn)單易行，適用范圍廣，收斂速度快，能有效的檢測(cè)出強(qiáng)噪聲背景下的高頻微弱信號(hào)，具有良好的工程應(yīng)用前景。 把采用粒子群算法的自適應(yīng)變步長(zhǎng)隨機(jī)共振融入到級(jí)聯(lián)雙穩(wěn)隨機(jī)共振中，提出了級(jí)聯(lián)雙穩(wěn)隨機(jī)共振自適應(yīng)降噪方法，通過對(duì)各級(jí)隨機(jī)共振參數(shù)的自適應(yīng)求解，實(shí)現(xiàn)了級(jí)聯(lián)隨機(jī)共振各級(jí)系統(tǒng)的自適應(yīng)輸出，最終達(dá)到對(duì)大參數(shù)條件下的工程信號(hào)進(jìn)行自適應(yīng)降噪的目的。將該方法應(yīng)用于仿真數(shù)據(jù)和工程實(shí)測(cè)數(shù)據(jù)，分析結(jié)果表明該方法能快速、有效的消除大參數(shù)信號(hào)里的高頻噪聲干擾，并突出低頻有用信號(hào)成分，降噪效果顯著。 將級(jí)聯(lián)雙穩(wěn)隨機(jī)共振自適應(yīng)降噪方法與經(jīng)驗(yàn)?zāi)Ｊ椒纸庀嘟Y(jié)合，提出了基于級(jí)聯(lián)雙穩(wěn)隨機(jī)共振自適應(yīng)降噪的經(jīng)驗(yàn)?zāi)Ｊ椒纸夥椒�，該方法可以消除�?jīng)驗(yàn)?zāi)Ｊ椒纸獾倪吔缧?yīng)并有效提高分解效率。在實(shí)驗(yàn)研究中，分別對(duì)原始輸入信號(hào)和級(jí)聯(lián)雙穩(wěn)各級(jí)輸出信號(hào)進(jìn)行了經(jīng)驗(yàn)?zāi)Ｊ椒纸猓ㄟ^對(duì)這些分解結(jié)果進(jìn)行比較分析，，可以發(fā)現(xiàn)該方法能在提高信號(hào)信噪比的同時(shí)，減少IMF分量的數(shù)量，并提高經(jīng)驗(yàn)?zāi)Ｊ椒纸獾馁|(zhì)量。
[Abstract]:Bistable stochastic resonance (bistable stochastic resonance) is a nonlinear phenomenon that occurs only when there is a certain synergy among noise, bistable system and input signal. For a certain large parameter noisy signal, it is necessary to jointly adjust the multiple parameters of stochastic resonance in order to produce stochastic resonance. However, there are few studies on the coupling among system parameters, signal frequency and noise intensity, and the joint parameter tuning of stochastic resonance is unguided, which can only depend on human experience. Therefore, the study of adaptive stochastic resonance is carried out in this paper, focusing on the difficult problem of multi-parameter joint regulation of stochastic resonance. In order to solve the problem that the parameter selection of stochastic resonance system depends on artificial experience and the difficulty of selecting step size for variable step size stochastic resonance calculation, an adaptive variable step stochastic resonance method based on particle swarm optimization algorithm is proposed. The SNR of the stochastic resonance output is taken as the fitness function of the particle swarm optimization algorithm. The adaptive solution of the variable step size stochastic resonance optimal output is realized by synchronously optimizing the system parameters and the computational step size. The simulation data and engineering data are used to verify the method. The analysis results show that the method is simple, wide and convergent, and can effectively detect the high frequency weak signal in the background of strong noise. It has a good prospect of engineering application. The adaptive variable step size stochastic resonance using particle swarm optimization algorithm is integrated into cascade bistable stochastic resonance, and an adaptive noise reduction method of cascade bistable stochastic resonance is proposed. The adaptive output of cascaded stochastic resonance system is realized, and the purpose of adaptive noise reduction for engineering signals under large parameters is achieved. The method is applied to the simulation data and the engineering measured data. The analysis results show that the method can eliminate the high frequency noise interference in the large parameter signal quickly and effectively, and highlight the low frequency useful signal components, and the noise reduction effect is remarkable. Combining the cascaded bistable stochastic resonance adaptive noise reduction method with empirical mode decomposition, an empirical mode decomposition method based on cascaded bistable stochastic resonance adaptive noise reduction is proposed. This method can eliminate the boundary effect of empirical mode decomposition and improve the efficiency of decomposition. In the experimental study, the original input signal and cascade bistable output signal are decomposed by empirical mode decomposition. By comparing and analyzing these decomposition results, it can be found that this method can improve the signal to noise ratio at the same time. Reduce the number of IMF components and improve the quality of empirical mode decomposition.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH133.33;TP18

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