Description: 一个用于建立温度模型的线性回归的温度模型算法-a temperature model for the establishment of the linear regression model algorithm temperature Platform: |
Size: 1346 |
Author:刘建锋 |
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Description: 一个用于建立温度模型的线性回归的温度模型算法-a temperature model for the establishment of the linear regression model algorithm temperature Platform: |
Size: 1024 |
Author:刘建锋 |
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Description: 利用回转窑生产氧化铝的工艺中，提出了一种新的基于SVR（支持向量回归）的烧成带温度软测量方法。即首先对从回转窑采集到的烧结图像进行预处理，消除噪声的干扰；然后利用大津方法分割出物料区和火焰区，并提取这些区域的图像特征；其次利用基于SVR方法建立的烧成带温度模型计算温度值；参数计算.-The use of alumina rotary kiln production process, a new-based SVR (support vector regression) of the burning zone temperature soft measurement method. That is first collected from the rotary kiln sintering image pre-processing to eliminate noise interference Otsu method and then use the materials division of the district and flame zones, and extraction of image features in these regions SVR-based method followed by the establishment of the burning zone temperature model temperature parameters. Platform: |
Size: 1024 |
Author:张曦月 |
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Description: 考察温度x对产量y的影响，测得下列10组数据：求y关于x的线性回归方程，检验回归效果是否显著，并预测x=42℃时产量的估值及预测区间（置信度95 ）
示例包含在内-X on the output of the temperature study of the impact of y, measured the following 10 sets of data: for y on x of the linear regression equation to test whether the effect of a significant regression and prediction when x = 42 ℃, and the valuation of yield prediction interval (confidence level 95 ) sample included Platform: |
Size: 4096 |
Author:马刚 |
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Description: （回归分析）
考察温度x对产量y的影响，测得下列10组数据：
求y关于x的线性回归方程，检验回归效果是否显著，并预测x=42℃时产量的估值及预测区间（置信度95 ）
-(Regression analysis) x on the output of the temperature study of the impact of y, measured the following 10 sets of data: for y on x of the linear regression equation to test whether the effect of a significant regression and prediction when x = 42 ℃, and the valuation of yield prediction interval (95 percent confidence level) Platform: |
Size: 25600 |
Author:mazhen |
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Description: 回归分析 考察温度x对产量y的影响 测得下列10组数据 求y关于x的线性回归方程 检验回归效果是否显著 并预测x=42℃时产量的估值及预测区间-Regression analysis, study the temperature on the yield y of x measured by the following 10 sets of data demand y on x, the linear regression equation of the regression effect is significant and predicted x = 42 ℃ when the output of the valuation and prediction interval Platform: |
Size: 17408 |
Author:Tiantian |
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Description: 多种时间序列数据的线性回归程序（比如气温、降水、径流等）-Multiple linear regression of time series data, procedures (such as temperature, precipitation, runoff, etc.) Platform: |
Size: 3072 |
Author:shuqi |
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Description: 利用matlab多元逐步回归分析方法建立初馏点温度的数学模型-Use of matlab multiple stepwise regression analysis method to establish early mathematical model of temperature at fractions Platform: |
Size: 1024 |
Author:苏涛 |
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Description: BP神经网络算法，可用于降水量、气温（包括平均气温、日最低最高气温等）、蒸发量等的回归预测。-BP neural network algorithm can be used for precipitation, temperature (including the average temperature, minimum maximum temperature, etc.), evaporation, etc. regression prediction. Platform: |
Size: 1024 |
Author:archonshine |
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Description: 485收发+DS18B20呼叫应答方式(ATMEL89C2051)
温度是工业生产中重要的物理参数之一，对其进行测量与控制是一项非常传统的工艺技术。采用数字温度传感器DS18B20和AT89C2051单片机进行温度数据的采集和存储，通过RS485总线传输。上位计算机对所测得的每个误差补偿量，依据误差回归模型的最小二乘法进行参数估计。做出线性误差补偿模型的数学方程，对常温下测量的数据进行了补偿处理，在-55℃到+125℃测量范围和测量精度0.2级下，实现传输距离达到500 m，并根据工程实践给出提高传输距离与测量精度的关键技术。-485 to send and receive+ DS18B20 call response mode (ATMEL89C2051)
Temperature is one of important physical parameters in industrial production, its measurement and control is a very traditional technique.Using digital temperature sensor DS18B20 and AT89C2051 single-chip temperature data collection and storage, through the RS485 bus transmission.Measured by computer for each error compensation amount, according to the error of the regression model of least squares method for parameter estimation.Make the mathematical equations of linear error compensation model, processing of the measured data of temperature compensation, in- 55 ℃ to+ 125 ℃ measurement range and measurement accuracy under 0.2, realize the transmission distance of 500 m, according to the engineering practice gives the key to improve the transmission distance and accuracy of measurement technology. Platform: |
Size: 48128 |
Author:weszn |
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Description: This project focuses on the energy consumption of electrical appliances which are normally used in a low energy consumption house. An IOT based Wireless Sensor is used to track the temperature and humidity conditions of the rooms of the house which are used to predict the electrical consumption of the house appliances. It is also significantly impacted by the weather conditions. In this paper, we try to predict the energy consumption of the appliances using various regression analysis models like Linear Regression, Random Forest, Support Vector Regressor(SVR), K-Neighbors Regressor and so on. Platform: |
Size: 2601471 |
Author:praba82 |
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Description: It is a machine learning based regression model implemented in python in which predicts the energy consumption of a house at a particular time span based on temperature and humidity of each rooms and other external factors such wind speed, visibility, dew point, etc. Platform: |
Size: 12342765 |
Author:praba82 |
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Search - temperature regression