Prediction Models of Air Outlet States of Desiccant Wheels Using Multiple Regression and Artificial Neural Network Methods Based on Criterion Numbers

2021 ◽  
pp. 117940
Author(s):  
Xianzhong Chen ◽  
Rang Tu ◽  
Ming Li ◽  
Xu Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Prediction Models ◽  
Network Methods ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

Modeling of process parameters to predict the efficiency of shallots stem cutting machine using multiple regression and artificial neural network

2021 ◽  
Author(s):  
Mariya Anthony Tito Anand ◽  
Sugumar Anandakumar ◽  
Akash Pare ◽  
Veerapandian Chandrasekar ◽  
Natarajan Venkatachalapathy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Process Parameters ◽  
Stem Cutting ◽  
Cutting Machine ◽  
Artificial Neural

scholarly journals Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

2018 ◽  
Vol 1 (1) ◽  
pp. 197-204
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Nonlinear Models ◽  
Linear Equations ◽  
Regression Method ◽  
Wave Resistance ◽  
Identification Accuracy ◽  
Linear Regression Method ◽  
Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression

2020 ◽  
Vol 6 (3) ◽  
pp. 1467-1475 ◽  
Author(s):  
Seyedeh Reyhaneh Shams ◽  
Ali Jahani ◽  
Mazaher Moeinaddini ◽  
Nematollah Khorasani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Multiple Regression ◽  
Artificial Neural
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