本文選題：糖尿病　切入點(diǎn)：粒子群優(yōu)化算法　出處：《北京化工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：糖尿病作為一種常見(jiàn)的內(nèi)分泌疾病,嚴(yán)重困擾了患者的日常生活并危害了患者的健康,長(zhǎng)期高血糖會(huì)引發(fā)一系列的并發(fā)癥。目前臨床上常用的方法是胰島素強(qiáng)化治療,可以分為每日多次皮下胰島素注射和胰島素持續(xù)皮下注射(胰島素泵)。胰島素持續(xù)皮下注射更接近于人體的生理模式。胰島素泵可以通過(guò)調(diào)整胰島素基礎(chǔ)率使血糖保持穩(wěn)定,通過(guò)輸入大劑量有效控制餐后引起的高血糖。盡管胰島素泵的應(yīng)用日益廣泛,但關(guān)于胰島素泵基礎(chǔ)量和大劑量輸注模式研究卻很少。臨床中常根據(jù)醫(yī)師的經(jīng)驗(yàn)和胰島素泵使用手冊(cè)的一些公式估算,然而糖尿病人差異很大,不同病人之間的體重差異很大,可以從50千克到100千克不等,不同病人之間對(duì)于胰島素的敏感性也不一樣,這些影響因素差異很大,不能為每個(gè)病人設(shè)計(jì)一個(gè)合理的胰島素基礎(chǔ)輸注率和大劑量,容易引起高血糖或低血糖的產(chǎn)生,危害糖尿病患者的生命。因此對(duì)基礎(chǔ)量和大劑量的優(yōu)化極為重要。本文提出了基于改進(jìn)的粒子群算法的優(yōu)化方法,首先提出了一種基于智能權(quán)重機(jī)制的粒子群算法,主要是提出了一個(gè)框架,是由任意搜索方法組合而成的,由一個(gè)時(shí)變的權(quán)重將其結(jié)合在一起,進(jìn)一步提高優(yōu)化的結(jié)果。該方法的核心是如何選取權(quán)重,本文權(quán)重的選取是與優(yōu)化效果成正比的。本文采用非均勻變異算子、微分變異算子和隨機(jī)局部搜索算法三種搜索算法,利用了非均勻變異的局部搜索的優(yōu)點(diǎn)、微分變異算子使粒子保持多樣性的優(yōu)點(diǎn)以及隨機(jī)局部搜索算法平衡局部搜索和全局搜索的能力的優(yōu)點(diǎn)。因此改進(jìn)粒子群算法有著很好的局部搜索和全局搜索的能力。本文對(duì)提出的新的粒子群算法進(jìn)行了性能測(cè)試,主要在15個(gè)基準(zhǔn)測(cè)試函數(shù)上進(jìn)行了詳細(xì)的測(cè)試,并與其它四個(gè)優(yōu)化算法的測(cè)試效果進(jìn)行了比較,仿真結(jié)果表明,相對(duì)于其它四個(gè)優(yōu)化算法,改進(jìn)的粒子群優(yōu)化算法具有很好的收斂速度和搜索精度。其次本文基于改進(jìn)粒子群算法提出了一種自動(dòng)調(diào)節(jié)胰島素泵基礎(chǔ)輸注率和大劑量的優(yōu)化方法,該方法能夠根據(jù)病人的血糖數(shù)據(jù)計(jì)算并自動(dòng)調(diào)節(jié)病人的基礎(chǔ)輸注率和大劑量,不需要人為的干預(yù),并在10個(gè)虛擬病人身上進(jìn)行了仿真測(cè)試,并且與其它四個(gè)優(yōu)化算法進(jìn)行比較,結(jié)果表明該方法能夠很好地控制病人的血糖,將病人血糖很快的控制在安全范圍內(nèi)。
[Abstract]:As a common endocrine disease, diabetes seriously disturbs patients' daily life and endangers their health. Long-term hyperglycemia can lead to a series of complications. At present, intensive insulin therapy is commonly used in clinical practice. It can be divided into multiple daily subcutaneous insulin injections and continuous insulin subcutaneous injections. Insulin continuous subcutaneous injection is closer to the physiological model of the human body. Insulin pumps can stabilize blood glucose by adjusting the insulin base rate. Effective control of postprandial hyperglycemia by infusion of large doses. Despite the increasing use of insulin pumps, However, little research has been done on the basic quantity of insulin pump and the model of high dose infusion. In clinical practice, we often estimate the basic amount of insulin pump and the formula of insulin pump use manual. However, there is a great difference in diabetes mellitus patients, and there is a great difference in body weight among different patients. It can range from 50 kg to 100 kg, and the sensitivity to insulin varies from patient to patient, and these factors vary greatly, and it's not possible to design a reasonable basal insulin infusion rate and a large dose for each patient. It is easy to cause hyperglycemia or hypoglycemia and endanger the lives of diabetic patients. Therefore, it is very important to optimize the basic quantity and large dose. In this paper, an optimization method based on improved particle swarm optimization (PSO) is proposed. Firstly, a particle swarm optimization algorithm based on intelligent weight mechanism is proposed. A framework is proposed, which is composed of arbitrary search methods and is combined by a time-varying weight. The core of this method is how to select the weight, and the selection of the weight is proportional to the optimization effect. In this paper, three search algorithms, namely, non-uniform mutation operator, differential mutation operator and random local search algorithm, are used. Take advantage of the advantage of local search of non-uniform variation, Differential mutation operator has the advantages of maintaining diversity of particles and the ability of stochastic local search algorithm to balance local search and global search. Therefore, the improved particle swarm optimization algorithm has good ability of local and global search. In this paper, the performance of the new particle swarm optimization algorithm is tested. The test results of 15 benchmark functions are compared with those of the other four optimization algorithms. The simulation results show that, compared with the other four optimization algorithms, The improved particle swarm optimization algorithm has good convergence speed and searching accuracy. Secondly, based on the improved particle swarm optimization algorithm, an optimization method is proposed to automatically adjust the basic infusion rate and large dose of insulin pump. This method can calculate and automatically adjust the basic infusion rate and large dose of patients according to the blood sugar data of patients, without the need of human intervention, and carry out simulation tests on 10 virtual patients, and compare it with the other four optimization algorithms. The results show that the method can control the blood sugar of the patients very well, and the blood sugar of the patients can be controlled quickly within the safe range.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：R587.1;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 陸菊明;;糖尿病研究現(xiàn)狀及展望[J];解放軍醫(yī)學(xué)雜志;2010年07期

2 趙曉東;王剛;;基于粒子群優(yōu)化算法的微波濾波器設(shè)計(jì)[J];計(jì)算機(jī)工程與應(yīng)用;2008年12期

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本文編號(hào)：1578749