A Blast Furnace Prediction Model Combining Neural Network with Partial Least Square Regression

2005 ◽  
Vol 76 (10) ◽  
pp. 694-699 ◽  
Author(s):  
Xiaojing Hao ◽  
Fengman Shen ◽  
Gang Du ◽  
Yansong Shen ◽  
Zhi Xie
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Prediction Model ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Model Combining

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

scholarly journals Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

2020 ◽  
Vol 33 (10) ◽  
pp. 1633-1641
Author(s):  
Dae-Hyun Lee ◽  
Seung-Hyun Lee ◽  
Byoung-Kwan Cho ◽  
Collins Wakholi ◽  
Young-Wook Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Carcass Weight ◽  
Body Measurements ◽  
Least Square Regression ◽  
Data Points ◽  
Hanwoo Cattle

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression
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