本文選題：隨機(jī)共振 + Duffing混沌振子�。� 參考：《南京信息工程大學(xué)》2017年碩士論文

【摘要】：在精密儀器測量中,信號往往會被各類噪聲所覆蓋,利用隨機(jī)共振特性,精確提取強(qiáng)噪聲背景下的微弱特征信號信息,實(shí)現(xiàn)極低信噪比狀態(tài)下的微弱信號檢測,成為近年來研究的熱點(diǎn)之一。本文以二維Duffing振子為隨機(jī)共振發(fā)生載體,研究了 Duffing混沌系統(tǒng)各參數(shù)誘導(dǎo)隨機(jī)共振問題,分別提出了基于全局人工魚群算法的自適應(yīng)隨機(jī)共振微弱信號檢測和基于Duffing振子隨機(jī)共振和遺傳算法的頻率調(diào)制微弱信號檢測方法,建立了檢測模型,通過仿真實(shí)驗(yàn),驗(yàn)證了檢測效果。具體研究如下:從隨機(jī)共振線性響應(yīng)和絕熱近似理論角度,推導(dǎo)了隨機(jī)共振產(chǎn)生及其限制條件。基于Duffing振子隨機(jī)共振特性,采用手動設(shè)置參數(shù)方法,實(shí)現(xiàn)了 α穩(wěn)定噪聲背景下隨機(jī)共振微弱信號檢測;在高斯白噪聲背景下,研究了雙穩(wěn)態(tài)系統(tǒng)參數(shù)對隨機(jī)共振模型結(jié)構(gòu)的影響;在α穩(wěn)定噪聲背景下,研究了二維Duffing振子系統(tǒng)各參數(shù)對隨機(jī)共振輸出的影響,為自適應(yīng)隨機(jī)共振系統(tǒng)搭建提供了理論支持。為克服傳統(tǒng)自適應(yīng)隨機(jī)共振單一參數(shù)優(yōu)化的缺陷,解決多參數(shù)調(diào)節(jié)問題,提出基于全局人工魚群算法的自適應(yīng)隨機(jī)共振微弱信號檢測。以系統(tǒng)全參數(shù)為優(yōu)化對象,比較了二維Duffing振子隨機(jī)共振和一維Langevin隨機(jī)共振的自適應(yīng)微弱信號檢測系統(tǒng)。結(jié)果表明:在相同條件下,二維Duffing隨機(jī)共振自適應(yīng)系統(tǒng)更具優(yōu)越性,并將基于Duffing振子的自適應(yīng)隨機(jī)共振系統(tǒng)成功應(yīng)用于海雜波背景下的微弱信號檢測,提升了海雜波背景下的小目標(biāo)信號檢測性能。針對隨機(jī)共振微弱信號檢測范圍有限、受小參數(shù)限制問題,根據(jù)頻率調(diào)制原理,提出基于二維Duffing隨機(jī)共振和遺傳算法的頻率調(diào)制微弱信號檢測。數(shù)值分析和仿真結(jié)果表明:所提方法靈活性強(qiáng),模型魯棒性好,能夠有效從強(qiáng)噪聲背景下提取微弱特征信號,不僅適用于高、低頻微弱信號檢測,還適用于多頻微弱信號的檢測。此研究擴(kuò)展了隨機(jī)共振微弱信號檢測范圍,為實(shí)際工程中的Duffing振子隨機(jī)共振微弱信號檢測提供依據(jù)。
[Abstract]:In the precision instrument measurement, the signal is often covered by various kinds of noise. Using the stochastic resonance characteristic, the weak characteristic signal information under the strong noise background is extracted accurately, and the weak signal detection under the extremely low signal-to-noise ratio is realized. It has become one of the hot research topics in recent years. In this paper, the problem of induced stochastic resonance of duffing chaotic system is studied by using two-dimensional duffing oscillator as the carrier of stochastic resonance. Adaptive stochastic resonance weak signal detection based on global artificial fish swarm algorithm and frequency modulation weak signal detection method based on duffing oscillator stochastic resonance and genetic algorithm are proposed respectively. The detection effect is verified. The main results are as follows: from the point of view of stochastic resonance linear response and adiabatic approximation, the generation of stochastic resonance and its limiting conditions are derived. Based on the stochastic resonance characteristics of duffing oscillator, the method of manually setting parameters is used to detect the weak signal of stochastic resonance in the background of 偽 stable noise, and in the background of Gao Si white noise, The influence of the parameters of bistable system on the structure of stochastic resonance model is studied, and the influence of the parameters of two-dimensional duffing oscillator system on the output of stochastic resonance is studied under the background of 偽 -stable noise, which provides theoretical support for the construction of adaptive stochastic resonance system. In order to overcome the shortcomings of traditional adaptive stochastic resonance single parameter optimization and solve the problem of multi-parameter adjustment, an adaptive stochastic resonance weak signal detection method based on global artificial fish swarm algorithm is proposed. The adaptive weak signal detection system with two-dimensional duffing oscillator stochastic resonance and one-dimensional Langevin stochastic resonance is compared. The results show that under the same conditions, the two-dimensional duffing stochastic resonance adaptive system is superior, and the adaptive stochastic resonance system based on duffing oscillator is successfully used to detect the weak signal in the background of Yu Hai clutter. The performance of small target signal detection in sea clutter background is improved. Aiming at the problem that the detection range of stochastic resonance weak signal is limited and limited by small parameters, according to the principle of frequency modulation, a frequency modulated weak signal detection method based on two-dimensional duffing stochastic resonance and genetic algorithm is proposed. Numerical analysis and simulation results show that the proposed method is flexible and robust, and can extract weak feature signals from strong noise background effectively, which is not only suitable for detection of high and low frequency weak signals. It is also suitable for the detection of multi-frequency weak signals. This study extends the detection range of stochastic resonance weak signal and provides the basis for duffing oscillator random resonance weak signal detection in practical engineering.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.23

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