本文關鍵詞： 眼血流自動調(diào)節(jié) 系統(tǒng)辨識 線性模型 非線性模型　出處：《哈爾濱商業(yè)大學》2016年碩士論文　論文類型：學位論文

【摘要】：目前,眼血流循環(huán)失調(diào)已被證實是許多眼部疾病的獨立危險因素之一,但是血流自動調(diào)節(jié)系統(tǒng)受損的直接原因并沒有明確定論。而且有關眼血流自動調(diào)節(jié)的研究并未深入探討眼血流系統(tǒng)的生理機制對眼血流自動調(diào)節(jié)系統(tǒng)進行建模研究。研究眼血流自動調(diào)節(jié)系統(tǒng),可以了解眼睛組織的供血機制,為眼部疾病的診斷、治療和預防提供依據(jù)。本研究的實驗數(shù)據(jù)是在cuff(下肢束帶法)條件下用LSFG(激光散斑血流成像)儀器采取眼血流信號,與此同時,用Finometer儀器對眼血壓信號進行同步采集。眼血流、眼血壓是能量集中在低頻的周期非平穩(wěn)隨機信號,在采集的過程中易受儀器、呼吸、肢體動作等干擾,通常帶有較強的噪聲。本文采用小波去噪等方法濾除信號的基線漂移、工頻干擾、肌電干擾等噪聲。眼血流自動調(diào)節(jié)機制是指在一定眼血壓范圍內(nèi),眼血流量保持相對恒定的生理機能。目前研究眼血流自動調(diào)節(jié)機制最新的方向是研究系統(tǒng)的線性或非線性模型。本文主要利用系統(tǒng)辨識技術建立眼血流自動調(diào)節(jié)系統(tǒng)系統(tǒng)模型。該技術中的模型參數(shù)辨識選用LSM算法。LSM算法是目前解決一些實際問題中使用最廣泛的經(jīng)典數(shù)據(jù)處理方法之一。因為在0.05～0.3Hz頻率范圍內(nèi),眼血壓與眼血流之間的關系更多地表現(xiàn)為線性關系。所以本文首先選用了線性ARX模型建立系統(tǒng)模型,然后轉(zhuǎn)化系統(tǒng)的傳遞函數(shù)參數(shù)模型并分析了模型的穩(wěn)定性。為了能夠全面的了解眼血流自動調(diào)節(jié)系統(tǒng)的線性特性與非線性特性,本文也嘗試建立系統(tǒng)的非線性模型。鑒于非線性模型可重復性差的特性,本文提出采用將線性和非線性相結(jié)合的Hammerstain模型建立系統(tǒng)模型。該模型的預測數(shù)據(jù)與實際數(shù)據(jù)擬合率為86.81%,而ARX模型的擬合率為83.92%。當使用另一組樣本驗證這兩個模型時,Hammerstain模型的擬合率為82.07%,而ARX模型的擬合率為63.7%。Hammerstain模型的可重復性優(yōu)于ARX模型,適用性更強。
[Abstract]:At present, ocular blood flow disorder has been proved to be one of the independent risk factors for many eye diseases. However, the direct cause of the damage of the automatic blood flow regulation system is not clear. Moreover, the research on the automatic regulation of the eye blood flow has not deeply discussed the physiological mechanism of the eye blood flow system, and the modeling of the automatic eye blood flow regulation system has been carried out. To study the automatic regulation system of eye blood flow, It can be used to understand the blood supply mechanism of eye tissue and to provide evidence for the diagnosis, treatment and prevention of ocular diseases. The experimental data of this study are to use the LSFG (laser speckle flow imaging) instrument to take eye blood flow signals under the condition of cuff (lower extremity banding method). At the same time, the Finometer instrument is used to synchronously collect blood pressure signals. Eye blood flow and eye blood pressure are non-stationary random signals whose energy is concentrated in low frequency. They are easily disturbed by instruments, breathing and limb movements. In this paper, wavelet denoising method is used to filter the baseline drift, power frequency interference, myoelectric interference, etc. The automatic regulation mechanism of eye blood flow is within a certain range of eye blood pressure. At present, the newest research direction of eye blood flow automatic regulation mechanism is to study the linear or nonlinear model of the system. This paper mainly uses the system identification technology to establish the eye blood flow automatic regulation. In this technique, LSM algorithm. LSM algorithm is one of the most widely used classical data processing methods to solve some practical problems, because in the frequency range of 0.05 ~ 0.3Hz, The relationship between eye blood pressure and eye blood flow is more linear. Therefore, the linear ARX model is used to establish the system model in this paper. Then the transfer function parameter model of the system is transformed and the stability of the model is analyzed. This paper also attempts to establish a nonlinear model of the system. In view of the poor repeatability of the nonlinear model, This paper presents a system model based on linear and nonlinear Hammerstain model. The fitting rate of the predicted data and the actual data of the model is 86.81, while the fitting rate of the ARX model is 83.92. When another set of samples is used to verify the two models, The fitting rate of Hammerstain model was 82.07, while that of ARX model was 63.7. The reproducibility of Hammerstain model was better than that of ARX model. The applicability is stronger.
【學位授予單位】：哈爾濱商業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R77

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