Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR)

Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions （MLR） and artificial neural networks （ANNs） are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations （nitrogen （N）, potassium （K）, calcium （Ca） and magnesium （Mg）） alone （P1）, com- bination of nutrients concentrations （P2）, nutrient concentration ratios alone （P3）, and combination of nutrient concentrations and nutrient concentration ratios （P4）. The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets （P1, P2 and P4）. However, the application of P3 （N/Ca ratio） as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error （RMSE） were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.

Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression

Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttings concentration in the wellbore annulus as a function of operational drilling parameters such as wellbore geometry, pumping rate, drilling fluid rheology and density and maximum drilling rate is very important for optimizing these parameters. This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict cuttings concentration during foam drilling operation. This model is applicable for various borehole conditions using some critical parameters associated with foam velocity, foam quality, hole geometry, subsurface condition (pressure and temperature) and pipe rotation. The average absolute percent relative error (AAPE) between the experimental cuttings concentration and ANN model is less than 6%, and using MLR, AAPE is less than 9%. A comparison of the ANN and mechanistic model was done. The AAPE values for all datasets in this study were 3.2%, 8.5% and 10.3% for ANN model, MLR model and mechanistic model respectively. The results show high ability of ANN in prediction with respect to statistical methods.

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μD, the energy of the highest occupied molecular orbital EHOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R2=91.28%, R2aj=89.11%, RMCE = 0.2831, R2ext=86.50% and R2=98.22%, R2aj=97.75%, RMCE = 0.1131, R2ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Study on the Model of Excessive Staminate Catkin Thinning of Proterandrous Walnut Based on Quadratic Polynomial Regression Equation and BP Artificial Neural Network

The excessive staminate catkin thinning （emasculation） of proterandrous walnut is an important management measure for improving yield. To improve the excessive staminate catkin thinning efficiency, the model of quadratic polynomial regression equation and BP artificial neural network was developed. The effects of ethephon, gibberel in and mepiquat on shedding rate of staminate catkin of pro-terandrous walnut were investigated by modeling field test. Based on the modeling test results, the excessive staminate catkin thinning model of quadratic polynomial regression equation and BP artificial neural network was established, and it was validated by field test next year. The test data were divided into training set, vali-dation set and test set. The total 20 sets of data obtained from the modeling field test were randomly divided into training set （17） and validation set （3） by central composite design （quadric rotational regression test design）, and the data obtained from the next-year field test were divided into the test set. The topological struc-ture of BP artificial neural network was 3-5-1. The results showed that the pre-diction errors of BP neural network for samples from the validation set were 1.355 0%, 0.429 1% and 0.353 8%, respectively; the difference between the predicted value by the BP neural network and validated value by field test was 2.04%, and the difference between the predicted value by the regression equation and validated value by field test was 3.12%; the prediction accuracy of BP neural network was over 1.0% higher than that of regression equation. The effective combination of quadratic polynomial stepwise regression and BP artificial neural network wil not only help to determine the effect of independent parameter but also improve the prediction accuracy.

Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models

Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.

Linearization Learning Method of BP Neural Networks

Feedforward multi layer neural networks have very strong mapping capability that is based on the non linearity of the activation function, however, the non linearity of the activation function can cause the multiple local minima on the learning error surfaces, which affect the learning rate and solving optimal weights. This paper proposes a learning method linearizing non linearity of the activation function and discusses its merits and demerits theoretically.

Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran)

Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as “dynamic regression” compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave velocity in wells without this parameter.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

基于改进海鸥优化算法的BP神经网络及其应用

针对传统反向传播(BP)神经网络在预测时随机产生的初始权值、阈值影响准确性的问题,提出一种改进的海鸥优化算法(ISOA)对BP神经网络的初始阈值、权值进行寻优。首先,为提高海鸥优化算法(SOA)的收敛精度和跳出局部最优的能力,使用Sine混沌映射初始化种群,引入非线性参数A,在海鸥攻击时引入乘除策略进行扰动,同时在攻击阶段后引入反向学习策略。然后,使用ISOA优化BP神经网络初始权值、阈值,解决对初值敏感和易陷入局部最优的问题。最后,在冻结裂隙砂岩动态冲击试验中进行峰值应力预测,结果表明:对比原始BP、粒子群优化(PSO)-BP和SOA-BP,ISOA优化后的BP神经网络对峰值应力预测精度更高。

基于回归分析和GA-BP神经网络算法的3D打印件弯曲性能预测

为进一步探究熔融沉积成型(FDM)3D打印参数和制件弯曲性能之间的关系,创建合理的FDM 3D打印制件弯曲强度预测模型。根据正交试验L_(16)(4^(5))的设计原则和神经网络算法模型的构建要求,按照不同分层高度、填充密度、打印温度、打印速度以及外壳厚度五种因素,制备25组试验试样,并进行弯曲性能检测。随后通过建立GA-BP神经网络模型、传统BP神经网络模型以及多元回归方程模型,分别对FDM 3D打印制件弯曲性能进行预测,并将预测数据与试验测试数据进行对比。通过对比发现,GA-BP神经网络模型预测数据与试验测试数据更为接近,其平均误差为3.71%,且误差值整体波动最小,BP神经网络模型与多元回归方程模型预测精度相差不大,BP神经网络模型预测平均误差为8.05%,多元回归方程模型预测平均误差为9.07%,但多元回归方程误差值整体波动最大。因此,采用GA遗传算法优化后的BP神经网络模型在进行FDM 3D打印制件弯曲性能预测方面具有更高的精度和更良好的稳定性。

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.

基于SSA-BP神经网络构建近红外光谱汽油辛烷值预测模型

基于100组汽油样品的近红外光谱分析数据,采用不同方法进行预处理,结合麻雀搜索算法(SSA)优化BP神经网络模型,构建了汽油辛烷值SSA-BP预测模型,对模型预测值与汽油研究法辛烷值(RON)测量值进行拟合,并与偏最小二乘法模型及BP神经网络模型的预测结果进行对比。结果表明:采用标准正态变量变换(SNV)方法进行光谱数据预处理后,所建SSA-BP模型的预测精度最高,验证集决定系数(R^(2))为0.9760,预测标准偏差(RMSEP)为0.326;3种汽油辛烷值预测模型中,SSA-BP神经网络模型预测准确度最好,且模型适用性和稳定性良好。说明利用SNV方法预处理光谱数据的SSA-BP神经网络模型,可以将近红外光谱分析技术更好地用于汽油辛烷值的预测,且预测结果具有良好的准确度,可以实现汽油辛烷值的快速无损检测。

基于线性回归及BP神经网络的RAT最大释放冲击载荷预测研究

冲压空气涡轮(Ram Air Turbine,RAT)最大释放冲击载荷是飞机结构设计重要参数。当前RAT释放冲击载荷的试验仅试飞或高速风洞试验可以得到,寻找一种有效的RAT最大冲击载荷预测方法很有必要。通过分析得到RAT最大释放冲击载荷的影响因素与飞行高度和飞行空速有直接关系,采用线性回归及BP神经网络研究飞行高度和空速对RAT最大释放载荷的影响,并从平均绝对误差及均方根百分误差进行评价。研究将试验及仿真结果作为训练样本,训练完成后将已知输入层参数输入后预测RAT最大释放载荷。对比预测最大释放载荷与试验结果,线性回归预测值平均绝对误差及均方根百分误差小于10%,BP神经网络预测值平均绝对误差及均方根百分误差的平均值小于5%。

An Early Warning Model of Financial Distress Prediction Based on Logistic-AHP-BP Neural Network Model

Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies in China has never exceeded 1% each year.The number of delisted companies in the security market is far less than the number of companies with financial distress.The capital market lacks a good delisting system and investors lack risk identification capabilities.Financial risk is directly related to delisting risk.Therefore,an early warning model of financial distress prediction for China.s stock market can provide guidance to stakeholders such as listed companies and capital markets.This paper first explains the immature delisting system of China.s capital market and the overall high risk of listed companies.financial distress.Then,the paper further elaborates previous research on financial distress prediction model of listed companies and analyzes the advantages and disadvantages of different models.This paper chooses the Analytic Hierarchy Process(AHP)to screen out the main factors that affect the risk of financial distress.The main factors are included in Logistic regression model and BP neural network model for predicting financial distress of listed companies.The overall effect of two models are assessed and compared.Finally,this paper proposes policy implications according to empirical results.

Global exponential stability analysis of cellular neural networks with multiple time delays

Global exponential stability problems are investigated for cellular neural networks （CNN） with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.

基于多模型BP神经网络算法的湿化器的湿度预测控制

高流量呼吸湿化器的湿度控制效果,是衡量湿化器品质的重要标准,由于湿化罐出气口没有使用湿度传感器实时采集数据,无法对湿化器出气口的空氧混合气体的湿度做实时监测,所以不能构建一个完整的闭环负反馈的系统,只能构建一个基于预估数据进行控制的开环系统。对于湿化器的湿度预测控制的问题,提出基于多模型切换的BP神经网络算法预测控制,在目标温度平衡点进行线性化,根据流量的变化选择模型。仪器测试实验表明:湿化器的湿度预测控制系统有更高的控制品质,适用于不同温度,不同流量的湿化模式。

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.