正交实验结合AHP和GA-BP神经网络优化益黄散醇提工艺

目的 优化益黄散的醇提工艺。方法 采用回流提取法,以乙醇体积分数、液料比、提取时间为考察因素设计正交实验,以橙皮苷、川陈皮素、橘皮素、没食子酸、诃黎勒酸、诃子酸、甘草苷、甘草酸、丁香酚含量和干浸膏得率为指标,采用层次分析法(AHP)进行赋权并计算综合评分。通过验证正交实验和遗传算法(GA)-反向传播神经网络(BP神经网络)所预测的结果确定益黄散最佳醇提工艺参数。结果 正交实验优选的最佳醇提工艺参数为乙醇体积分数60%、液料比14∶1(mL/g)、提取时间90 min、提取2次,验证所得综合评分为79.19分;GA-BP神经网络优选的最佳醇提工艺参数为乙醇体积分数65%、液料比14∶1(mL/g)、提取时间60 min、提取2次,验证所得综合评分为85.30分,高于正交实验所得结果。结论 采用正交实验结合GA-BP神经网络的寻优方法较传统的正交实验寻优方法效果更佳,其优选出的益黄散最佳醇提工艺稳定可靠。

小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别

井架钢结构损伤影响其承载安全性,为快速、准确对损伤位置进行识别,提出小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别方法。首先利用小波包处理非平稳振动信号的优良性能对原始振动信号进行特征提取,获得表征井架钢结构损伤的信息;再通过特征参数建立数据集训练并测试井架钢结构损伤识别模型,该模型结合遗传算法自身特点改善了传统BP神经网络的不足。本文识别方法不需要损伤前的数据特征进行对比,便可对损伤位置进行确定。经过对石油井架钢结构模型实验验证:该方法对井架钢结构损伤识别准确率超过90%,相对于BP网络识别准确率以及识别速度均有所提高。

基于BP神经网络算法的异步电机故障诊断系统研究

为了确保电机安全可靠地运行,研究了BP神经网络算法对异步电动机进行故障诊断。通过MATLAB平台,分别使用附加动量因子和自适应学习率两种梯度下降法进行网络训练,搭建故障诊断BP网络模型。以MSE值为指标优化最佳隐含层节点数、动量因子与学习率,并通过遗传算法来优化BP网络的初始权值,对故障测试样本进行仿真测试。结果表明,GA-BP网络模型比MF-BP和AG-BP的MSE值更低,仅为0.009163,优化后的诊断预测结果与目标值几乎没有差别。基于遗传算法改进的故障诊断系统模型能够满足异步电动机故障诊断的应用需求。

BP神经网络算法在求解数学建模最优化问题中的应用

为了解决目标函数较为复杂、无法用初等函数表示的最优化问题,文章采用了结合BP神经网络与遗传算法的方法进行求解。求解过程分为两个模块:第一,利用BP神经网络算法确定目标函数的解析式;第二,利用遗传算法寻找目标函数的最优解。为验证该方法的可行性,文章对单变量和多变量两种情况进行了验证。

基于改进PSO-BP神经网络的热采管柱应力预测

稠油热采过程中,油套管柱由于在温度、地层等多重载荷作用下发生塑性形变进而导致断裂或失效。文中根据热采管柱高温服役工况,引入异步变化学习因子和自适应权重建立输入参数为注汽温度、井深、非均匀系数和水泥环温度,输出参数为套管应力的改进PSO-BP模型。文中以N80热采套管为例,选取260、280、300、320、340℃5种温度工况下有限元模拟结果作为训练数据,对比BP模型、GA-BP模型、MEA-BP模型、PSO-BP模型和改进PSO-BP模型在300℃工况温度下井深200、300、400、500、600、700 m处套管应力的预测值和试验值、有限元计算值。结果表明:改进PSO-BP模型预测的应力与试验值最接近,最大和最小误差分别为2.69%和0.06%。最后从训练数据、预测误差、计算时间等方面对建立的改进PSO-BP模型进行了评价,为热采管柱服役过程中的强度安全分析提供智能高效的模型。

基于GA-BP神经网络的新疆南疆核桃树生长模型研究

文章提出了一种利用遗传算法优化BP神经网络的核桃树生长模型来预测核桃树的树高、胸径的方法,通过优化BP神经网络的权值和阈值建立GA-BP模型,与多元线性回归模型的预测结果进行比较。结果表明:采用遗传算法优化后的模型具有更高的预测精度,对核桃树生长预测具有指导意义。

基于FOA-BP-AdaBoost的大坝变形预测模型及应用

为提升大坝变形监测预测精度,解决变形量受多因素影响等问题,笔者提出了基于果蝇优化算法(FOA)、BP神经网络的AdaBoost强预测组合模型(FOA-BP-AdaBoost),并与BP神经网络模型、FOA-BP神经网络模型应用于工程实例中的预测精度进行多方位量化对比。结果表明:强预测模型集齐了果蝇算法全局优化、BP神经网络局部寻优和AdaBoost“优中选优”的特点,最大程度优化了预测效果;实例应用证实了FOA-BP-AdaBoost模型在大坝变形预测领域的准确性和有效性。该模型已成功应用于工程实例,可为类似工程提供参考。

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

基于GA优化BP神经网络的小电流接地故障选线方法

将GA优化BP神经网络的算法引入到小电流接地故障选线方法中。文中基于MATLAB进行仿真试验,通过小波包法、五次谐波法、基波比幅比相法及零序有功功率法等传统选线方法,将零序电流信号的各种特征量进行提取,经过故障测度函数计算得到故障测度数据,将数据分别输入到GA-BP神经网络与单一BP神经网络进行训练和测试,讨论GA-BP神经网络算法与单一BP神经网络算法选线性能的差异,输出故障选线结果并与基于各选线方法的故障测度数据进行对比。结果表明,综合多种传统选线方法的GA-BP神经网络准确率明显高于传统选线方法,且其选线速度与精度优于单一BP神经网络,能够更快速、有效地进行故障选线,满足配电网故障选线要求。

基于5参数GA-BP模型的出水水质预测——以宁夏某水厂为例

为解决饮用水处理过程中关键水质参数浊度取样检测时滞性大和精度低的问题,提出了基于遗传算法优化BP神经网络(GA-BP)的出水浊度预测模型。利用2019—2021年银川市某水厂的实测出水浊度及相关水质数据,采用灰色关联度分析对影响出水浊度的输入指标进行筛选,结合Q型聚类分析将样本数据划分为具有不同特征的3类,构建了基于GA-BP神经网络的机器学习模型对出水浊度进行预测,并与传统BP和未分类的预测结果进行对比。结果表明:与未分类相比,利用Q型聚类分析后预测模型的误差评价指标决定系数(R2)和均方根误差(RMSE)分别优化了2.9%和22%;与传统BP神经网络相比,经遗传算法优化后的预测模型误差评价指标R2和RMSE分别优化了2.4%和12%。研究表明,Q型聚类分析和遗传算法均能提高BP神经网络预测模型的泛化能力,减小误差。

基于BP人工神经网络与遗传算法的航速优化

为了进一步提高船舶能耗效率,本文提出一种基于BP人工神经网络与遗传算法的航速优化技术路线。首先,介绍常见油耗模型的构建方法;其次,利用BP人工神经网络建立目标船舶的油耗模型。模型预测的平均绝对误差为2.3%,准确度和泛化能力基本满足工程应用要求。最后,利用遗传算法,并基于历史气象数据对目标船舶的航线做分段航速优化。计算结果表明,航速优化后目标船舶的航行时长不仅能减少1.35天,燃油损耗还可节省10.1%,由此说明对航行船舶做分段航速优化是一种可行方案。

基于仿生算法联合优化BP的燃煤发热量预测

【目的】燃煤发热量的精准预测和评价是煤质分析和热工计算的重要基础,目前神经网络预测燃煤发热量的模型虽能有效拟合非线性关系,但存在易陷入局部最小值、收敛速度慢等问题。【方法】为精准预测煤炭在工业锅炉燃烧过程中的发热量,提出一种由仿生算法FA-GA联合优化BP神经网络的燃煤发热量预测方法。将774组燃煤锅炉常用煤的工业分析及元素分析数据进行预处理,根据平均影响值对煤质指标进行特征变量筛选,最终建立FA-GA-BP的发热量预测模型,并从误差评价指标、迭代次数等方面对优化算法寻优能力和模型预测精度进行了检验。【结果】经过特征变量筛选后模型的预测精度提升至0.956 1;FA-GA联合算法与单一优化算法FA、GA、PSO相比,迭代次数显著减少,算法的全局搜索能力得到有效提升;FA-GA-BP模型与单一优化模型FA-BP、GA-BP、PSO-BP以及目前常用的发热量模型MLR和SVR相比,精度更高,相关系数可达0.984 5.【结论】FA-GA算法优化BP模型,针对燃煤锅炉中来自不同地区、不同煤种的发热量预测具有良好的效果,在理论上满足了工业误差要求。改进后的燃煤发热量预测模型可为有效监测入炉煤质实时变化提供一种新方法。

Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.

Optimization of Biodynamic Seated Human Models Using Genetic Algorithms

Many biodynamic models have been derived using trial and error curve-fitting technique, such that the error between the computed and measured biodynamic response functions is minimum. This study developed a biomechanical model of the human body in a sitting posture without backrest for evaluating the vibration transmissibility and dynamic response to vertical vibration direction. In describing the human body motion, a three biomechanical models are discussed (two models are 4-DOF and one model 7-DOF). Optimization software based on stochastic techniques search methods, Genetic Algorithms (GAs), is employed to determine the human model parameters imposing some limit constraints on the model parameters. In addition, an objective function is formulated comprising the sum of errors between the computed and actual values (experimental data). The studied functions are the driving-point mechanical impedance, apparent mass and seat- to-head transmissibility functions. The optimization process increased the average goodness of fit and the results of studied functions became much closer to the target values (Experimental data). From the optimized model, the resonant frequencies of the driver parts computed on the basis of biodynamic response functions are found to be within close bounds to that expected for the human body.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

基于遗传算法优化BP神经网络的生石膏超细磨预测效果研究

为提高BP神经网络对生石膏超细磨效果的预测准确性,采用Pearson相关系数对超细石膏粉体正交试验产品细度与影响因素的显著性进行分析,并利用遗传算法优化BP神经网络对超细石膏粉体试验产品的d_(50)和d_(90)进行预测,结果表明:超细石膏粉体制备过程中影响细度因素的显著性由大到小依次为排矿口宽度、矿浆质量分数和超细磨时间。利用排矿口宽度和矿浆质量分数两个主要影响因素,利用遗传算法对BP神经网络进行优化,与未优化的BP神经网络相比,经遗传算法优化的BP神经网络具有更高的精度,预测误差也更小,其d_(50)平均绝对误差为0.7575,均方误差为0.7977,均方误差根为0.8931,平均绝对百分比误差为4.4838%;d_(90)平均绝对误差为0.7870,均方误差为0.8294,均方误差根为0.9107,平均绝对百分比误差为1.6658%。研究成果可为超细粉体的制备提供参考。

基于GA-BP神经网络的致密砂岩横波时差预测方法

横波时差资料对开展致密砂岩储层水平井井壁稳定性与压裂效果研究有着关键作用。受开发成本的制约,横波时差测井资料极少,对研究致密砂岩力学性质造成很大困难。以井径、自然伽马和纵波时差等常规测井资料为基础,提出了基于GA-BP神经网络的致密砂岩横波时差预测方法。利用定边油田L区D166井长7、长8段数据,分别进行了GA-BP模型和BP模型的训练和检验,并对比分析了2种模型的预测效果。结果表明,GA-BP模型不受井眼环境、岩性和沉积环境等因素的影响,平均绝对百分比误差较BP模型小3.109个百分点,精准性更高、泛化性更强、可靠性更好。该方法对提高横波时差预测精度具有实际应用价值,为后续研究奠定了基础。

基于SSA-GA-BP神经网络的数显千分表非线性误差补偿

利用数显千分表进行精密测量时,零部件的生产、装配及使用磨损、挤压、碰撞等带来的固有误差与弹性误差严重降低了测量精度。针对此问题,利用遗传算法(genetic algorithm,GA)寻优速度快、精度高、并行搜索能力的优势及麻雀搜索算法(sparrow search algorithm,SSA)的全局寻优性能,优化反向传播(back propagation,BP)神经网络的初始权值、阈值及网络结构等,提出了基于数显千分表测量数据非线性误差补偿的SSA-GA-BP神经网络模型。将其与传统BP神经网络、遗传算法优化的GA-BP神经网络进行比较分析。结果表明:所提出SSA-GA-BP神经网络可使数显千分表的非线性误差由没有补偿前的最大误差5.504μm降低至0.883μm,残差平方和、相对误差和R相关系数具有一定的优越性。

GA-BP神经网络在发射场的气温预报应用研究

为减小发射场气温预报误差,将BP神经网络和遗传算法结合起来,以2018—2022年的欧洲细网格气温预报数据和发射场实况数据为基础,利用相关系数筛选预报因子,建立了发射场气温预报模型。结果表明:模型气温预报平均绝对误差为1.132℃,较业务使用的欧洲细网格预报平均绝对误差优化了7.8%;模型气温预报的误差标准差为0.907℃,模型能较好修正欧洲细网格的温度预报误差离散值,较欧洲细网格预报的误差更稳定,能大幅减小预报员的人工订正工作;在神舟15号任务保障中,该模型预报的窗口温度为-18.02℃,而实况为-17.9℃,在临界条件下温度精细化预报保障提供了一种可靠性较高的预报手段。