Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.

Surface Quality Evaluation of Fluff Fabric Based on Particle Swarm Optimization Back Propagation Neural Network

Aiming at the problem that back propagation(BP)neural network predicts the low accuracy rate of fluff fabric after fluffing process,a BP neural network model optimized by particle swarm optimization(PSO)algorithm is proposed.The sliced image is obtained by the principle of light-cutting imaging.The fluffy region of the adaptive image segmentation is extracted by the Freeman chain code principle.The upper edge coordinate information of the fabric is subjected to one-dimensional discrete wavelet decomposition to obtain high frequency information and low frequency information.After comparison and analysis,the BP neural network was trained by high frequency information,and the PSO algorithm was used to optimize the BP neural network.The optimized BP neural network has better weights and thresholds.The experimental results show that the accuracy of the optimized BP neural network after applying high-frequency information training is 97.96%,which is 3.79%higher than that of the unoptimized BP neural network,and has higher detection accuracy.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

A Method for Solving Computer-Aided Product Design Optimization Problem Based on Back Propagation Neural Network

Because of the powerful mapping ability, back propagation neural network (BP-NN) has been employed in computer-aided product design (CAPD) to establish the property prediction model. The backward problem in CAPD is to search for the appropriate structure or composition of the product with desired property, which is an optimization problem. In this paper, a global optimization method of using the a BB algorithm to solve the backward problem is presented. In particular, a convex lower bounding function is constructed for the objective function formulated with BP-NN model, and the calculation of the key parameter a is implemented by recurring to the interval Hessian matrix of the objective function. Two case studies involving the design of dopamine β-hydroxylase (DβH) inhibitors and linear low density polyethylene (LLDPE) nano composites are investigated using the proposed method.

Preparation of ZrB_2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.

基于DBO-BP的工业机器人定位误差补偿方法

为提高工业机器人绝对定位精度,提出一种基于DBO-BP与离线前馈校正相结合的方法。该方法适用于工业机器人定位误差补偿研究。通过使用拉丁超立方抽样法获取工业机器人的位姿样本,并利用BP神经网络建立误差预测模型,应用DBO优化算法改善了局部最优现象,从而提高了模型的收敛性和鲁棒性。经过离线前馈补偿处理后,降低了工业机器人定位误差,大幅提高了机器人绝对定位精度。这种方法能够有效提高机器人的精度和稳定性,并为工业机器人的精准定位问题提供了可行的解决方案。

基于PSO-BP神经网络的分拣机器人视觉反馈跟踪

针对分拣机器人视觉反馈跟踪精度差、耗时较长的问题,研究基于粒子群算法-反向传播(particle swarm optimization-back propagation,PSO-BP)神经网络的分拣机器人视觉反馈跟踪方法,以提升视觉反馈跟踪效果。依据分拣机器人的视觉反馈信息,建立分拣机器人运动学模型,并求解分拣机器人机械臂输出位置和输入位置的误差函数;利用PSO算法优化BP神经网络的权值与偏置;在权值与偏置优化后的BP神经网络内,输入误差函数,预测分拣机器人视觉反馈跟踪控制量;利用预测视觉反馈跟踪控制量,在线调整增量式比例-积分-微分(proportional-integral-derivative,PID)的参数,输出高精度的分拣机器人视觉反馈跟踪控制量,实现分拣机器人视觉反馈跟踪。实验结果表明,该方法可有效视觉反馈跟踪分拣机器人机械臂的关节角;存在干扰情况下,在运行时间为10 s左右时,阶跃响应趋于稳定;有干扰情况下,视觉反馈跟踪的平均误差为0.09 cm,耗时平均值为0.10 ms;无干扰情况下,平均误差为0.03 cm,耗时平均值为0.04 ms。

基于PROA-BP的激光3D投影振镜偏转电压预测模型

为减小激光3D投影系统振镜偏转角偏差与根据振镜偏转角标定的转轴公垂线长度e误差引起的投影系统综合非线性误差,实现激光3D投影系统高精度辅助装配,提出一种基于改进的?鱼优化算法-BP神经网络的激光3D投影振镜偏转电压预测模型,以激光出射方向单位矢量作为输入预测振镜偏转电压数值。将改进的?鱼算法与BP神经网络相结合,解决BP神经网络容易陷入局部最优解问题,并通过BP神经网络实现激光3D投影系统综合非线性误差的耦合与补偿。结果表明,改进的?鱼算法-BP神经网络训练10 000次后均方差误差和平均绝对误差均值分别是粒子群算法-BP神经网络的41.2%、62.4%,是BP神经网络的22.2%、50.7%。基于改进的?鱼算法-BP激光3D投影振镜偏转电压模型的投影定位精度为0.35 mm,与激光3D投影传统模型相比,投影定位精度提升了30%,可实现更高精度投影定位。

地表沉陷预测的改进BP神经网络模型

为了更加准确地预测地表沉陷变形,基于Adaboost算法采用多网络共同计算策略改进了BP神经网络,通过实际沉降数据对Adaboost算法改进后的神经网络进行训练,预测地表最大下沉量、影响角正切和拐点偏移距,将预测的3个参数代入概率积分法中,建立了地表沉陷公式,对改进效果和地表沉陷公式分别进行了验证。结果表明:(1)通过对比改进前后BP神经网络的计算精度,未经过Adaboost算法改进的BP神经网络误差明显大于改进后的BP神经网络,说明基于Adaboost修正后的BP神经网络计算精度得到了有效提升;(2)基于BP神经网络对最大下沉量、影响角正切和拐点偏移距3个参数进行预测,结合概率分析法,能够实现稳沉后采空区主断面上方地表沉降规律的准确描述。以鲁西南地区某矿3301采空区地表为例,利用改进BP神经网络预测了地表最大下沉量、影响角正切和拐点偏移距,进而给出了地表沉陷曲线,与现场实测结果对比显示:改进BP神经网络的最大误差小于0.105 m,最大相对误差为4.3%,证明了所提计算方法的可靠性。

基于BA-BP的汽车同步器齿毂误差溯源

同步器齿毂是汽车变速器装置的重要零件,其加工质量对变速器的性能、可靠性有直接影响。针对人工经验判断齿毂误差源范围效率较低的问题,本文提出一种基于蝙蝠算法优化BP神经网络的误差溯源方法,分析齿毂加工过程中的误差来源,利用蝙蝠算法对权值和阈值进行优化,获取最优值后构造BA-BP误差溯源模型,并采集数据样本对模型进行验证并与未优化之前的BP神经网络的误差溯源方法进行对比。与未优化之前BP神经网络溯源模型准确率83.56%相比,优化后的准确率为96.34%,该方法使溯源准确率明显提高,支持生产人员对后续的超差工件进行误差原因追溯,对生产过程中存在的问题直接进行处理排除,提高生产效率。

PREDICTION OF FLOW STRESS OF HIGH-SPEED STEEL DURING HOT DEFORMATION BY USING BP ARTIFICIAL NEURAL NETWORK

The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.

基于BSO-BP的船舶油耗预测模型

为解决基于传统反向传播(back propagation,BP)神经网络的船舶油耗预测模型易陷入极小值和误差较大的问题,提出一种利用头脑风暴优化(brain storm optimization,BSO)算法优化BP神经网络的船舶油耗预测模型(简称BSO-BP模型)。以“维多利亚凯娅”号内河游船为研究对象,将BSO-BP模型的预测结果与采用传统BP神经网络以及模拟退火(simulated annealing,SA)算法、遗传算法(genetic algorithm,GA)、粒子群优化(particle swarm optimization,PSO)算法优化的BP神经网络的船舶油耗预测模型的预测结果进行对比分析。结果表明:与传统BP神经网络模型的预测结果相比,BSO-BP模型预测结果的可决系数R^(2)提高了0.003 9,均方误差、均方根误差、平均相对误差、平均绝对误差分别降低了0.034 4、0.154 1、0.010 2、0.017 8,说明在船舶油耗预测中BSO算法对BP神经网络的预测精度有显著的提升作用;BSO-BP模型预测结果的各项评价指标在所对比的5种模型中均表现最好,说明与SA算法、GA和PSO算法相比,BSO算法对BP神经网络的提升效果更好。

一种LM-BP加速搜索的周跳探测与修复方法

针对传统三频周跳探测与修复方法中的不敏感、漏检以及效率较低等问题,提出一种基于莱文伯格-马夸特(LM)-反向传播(BP)神经网络加速搜索法的伪距相位组合与电离层残差组合联合周跳探测与修复方法:利用2个伪距相位组合以减少不敏感周跳数量,利用1个电离层残差组合以提高小周跳探测敏感度;在构成3个线性无关的组合观测值后,使用LM-BP加速搜索算法进行周跳探测与修复。实验结果表明,相对常规的伪距相位组合与电离层残差组合联合方法,该方法能够提高周跳探测与修复性能,可探测小至1个的周跳,探测与修复整体时效有较大提升。

CNC Thermal Compensation Based on Mind Evolutionary Algorithm Optimized BP Neural Network

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.

基于BP神经网络的谐波减速器柔轮疲劳寿命预测研究

柔轮是谐波减速器的易损零件,在波发生器的高转速带动下转动,其疲劳寿命一直是备受关注的研究重点。以某型号杯型谐波减速器柔轮为研究对象,建立有限元仿真模型,得到柔轮最大应力与筒长、筒体壁厚和不同过渡圆角半径等参数之间的关系。根据柔轮S-N曲线,计算得到柔轮疲劳寿命,利用反向传播(Back Propagation,BP)神经网络实现了柔轮疲劳寿命的预测。

基于BP神经网络的高分辨率海底地形跨层生成模型

为了满足海底地形的高分辨率需求及解决测量数据的有限性问题,基于多层前馈神经网络(back propagation,BP)和跨层网格生成策略,建立了兼顾海底区域地形整体特征和局部地形信息的海底地形跨层生成模型,实现对海底地形数据生成填充。以南海海底地形为例,通过误差对比、假设检验以及海底地形云图的图像清晰度对本文模型生成数据进行有效性验证。结果显示所建立的模型在保证与原始数据之间误差小和数据特征相同的前提下完成了对地形云图的图像清晰度的提升,并且结果优于传统克里金插值方法。本文分析结果可为地形数据相关研究提供参考。

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。

基于煤岩煤质多元指标的BP神经网络焦油产率预测方法研究

【目的】焦油产率是煤低温干馏利用最重要的煤质参数,决定着富油煤的清洁利用方向。但由于多方面的原因,在煤炭地质勘查阶段对煤焦油产率的测试数据十分有限,极大地制约了富油煤的精细评价和高效利用。【方法】为了提高富油煤精细评价的科学性和准确性,以陕北侏罗纪煤田以往测试1073组煤岩煤质数据为基础,并筛选出显微组分、工业分析、元素分析、灰成分分析等20项煤岩煤质参数齐全的141组数据,利用BP神经网络算法分别建立了20项煤岩煤质指标的焦油产率预测模型和以4项工业分析为基础的焦油产率预测模型,并对预测模型的准确性和合理性进行分析评价。【结果和结论】结果表明:以20项煤岩煤质指标为特征建立的预测模型最终训练均方误差为0.30,测试集数据预测结果平均绝对误差为0.65;以4项工业分析指标为特征建立的预测模型最终训练均方误差为1.07,测试集数据预测结果平均绝对误差为1.35;扩展集数据在两个模型中预测结果平均绝对误差分别为0.84和1.34,显示出20项煤岩煤质指标比4项工业分析煤质指标建立的预测模型具有更高的拟合优度和泛化性能。利用SHAP算法进一步对预测模型中20项煤岩煤质指标的重要性进行量化分析,显示出镜质组、氢元素、三氧化二铁、水分、挥发分、碳元素、壳质组、氧元素含量是焦油产率的正向影响因素,三氧化二铝、惰质组、固定碳、灰分、二氧化硅含量是焦油产率的负向影响因素,模型中煤岩煤质与焦油产率之间的内在联系很好地契合了地质上对焦油产率影响因素的基本认识,该焦油产率预测模型可以很好地应用于陕北侏罗纪煤田的焦油产率预测,为陕北地区富油煤的清洁高效利用提供支撑。

基于GRU-BP算法的高精度动态物流称重系统

针对动态物流秤测量精度对载重、采样频率、带速较为敏感的问题,提出了一种高精度动态物流称重系统。首先,采用三因素五水平正交试验法,结合皮尔逊相关性检验原则,使用低通巴特沃斯与卡尔曼滤波器对传感器压力信号进行了滤波降噪处理,并将加速度信号作为模型输入信号,进行了特征补偿;然后,基于深度学习算法,提出了一种改进的门控循环单元模型,在该模型采样区间内将压力与振动改写为时序化信号,并将其共同输入门控循环单元(GRU)模型;最后,对GRU模型进行了改进,对其结构输出了层堆叠误差反向传播神经网络(BP),有效加强了模型的非线性映射能力。研究结果表明:在各类传动速度及测试货物下,该模型的最大测量误差相对于同类型深度学习模型长短期记忆(LSTM)神经网络、循环神经网络(RNN)时序模型及传统数值平均模型的误差,依次降低了16.14%、27.14%、76%,可用于各类称重系统。