【摘要】：高壓氧醫(yī)學(xué)已經(jīng)是一門比較成熟的學(xué)科,并且已經(jīng)被廣泛應(yīng)用在臨床醫(yī)學(xué)上。目前新建的大型氧艙中有很多已經(jīng)采用了計(jì)算機(jī)控制技術(shù),實(shí)現(xiàn)控制過程中的自動(dòng)控制。但是此項(xiàng)控制技術(shù)還只是停留在比較淺顯的PID控制的層面,并沒有將計(jì)算機(jī)的潛在的巨大的能量發(fā)揮出來。隨著神經(jīng)元網(wǎng)絡(luò)技術(shù)及模糊控制的發(fā)展,我們可以嘗試著研發(fā)出新型的控制技術(shù),以便使高壓氧艙的工作效率達(dá)到最佳值。本文對高壓氧艙中氧氣的濃度控制做了深入的研究,完成了幾項(xiàng)主要的工作：(1)對高壓氧艙中的氧氣系統(tǒng)建立了動(dòng)態(tài)數(shù)學(xué)模型,根據(jù)數(shù)學(xué)模型可以總結(jié)出氧氣的放散與制氧機(jī)參數(shù)之間的關(guān)系。(2)對氧氣的短期負(fù)荷進(jìn)行了研究,確定了人工神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)及算法,選擇了歷史數(shù)據(jù)對網(wǎng)絡(luò)進(jìn)行了訓(xùn)練,得到了令人滿意的訓(xùn)練結(jié)果；(3)對高壓氧艙的高壓環(huán)境內(nèi)氧氣的濃度進(jìn)行控制,最終確定了了模糊控制算法可以使輸出結(jié)果達(dá)到較好的控制精度；(4)對高壓氧艙所用的氧氣傳感器進(jìn)行了故障檢測,具體的實(shí)現(xiàn)是采用了人工神經(jīng)網(wǎng)絡(luò)的算法,經(jīng)過反復(fù)多次訓(xùn)練,取適當(dāng)?shù)膶W(xué)習(xí)系數(shù),可使均方誤差達(dá)到最小。試驗(yàn)與實(shí)踐證明,該網(wǎng)絡(luò)具有良好的收斂性和穩(wěn)定性。
[Abstract]:Hyperbaric oxygen medicine is a mature subject and has been widely used in clinical medicine. At present, many of the new large oxygen tanks have adopted computer control technology to realize automatic control in the process of control. However, the control technology is still in the superficial level of PID control, and does not bring the potential of the computer into full play. With the development of neural network technology and fuzzy control, we can try to develop a new control technology in order to optimize the working efficiency of hyperbaric oxygen chamber. In this paper, the control of oxygen concentration in hyperbaric oxygen chamber has been deeply studied, and several main tasks have been completed: (1) the dynamic mathematical model of oxygen system in hyperbaric oxygen chamber has been established. According to the mathematical model, the relationship between oxygen release and oxygen generator parameters can be summarized. (2) the short-term load of oxygen is studied, the structure and algorithm of artificial neural network are determined, and the historical data are selected to train the network. Satisfactory training results have been obtained; (3) to control the oxygen concentration in the high pressure environment of the hyperbaric oxygen chamber, and finally determine the fuzzy control algorithm which can make the output result reach a better control precision; (4) the fault detection of oxygen sensor used in hyperbaric oxygen chamber is carried out. The algorithm of artificial neural network is adopted. After repeated training and appropriate learning coefficient, the mean square error can be minimized. The experiment and practice show that the network has good convergence and stability.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：R459.6;TP273

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本文編號(hào)：2472359