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

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.

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

Journal of Food Process Engineering ◽

10.1111/jfpe.13944 ◽

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

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On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces

Modern Applied Science ◽

10.5539/mas.v4n8p3 ◽

2010 ◽

Vol 4 (8) ◽

Cited By ~ 5

Author(s):

Igor Esau

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Large Eddy Simulations ◽

Large Eddy ◽

Network Methods ◽

Artificial Neural

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The integration of fuzzy logic and artificial neural network methods for mobile robot obstacle avoidance in a static environment

2013 IEEE 3rd International Conference on System Engineering and Technology ◽

10.1109/icsengt.2013.6650193 ◽

2013 ◽

Cited By ~ 7

Author(s):

Muhammad Akmal Jeffril ◽

Nohaidda Sariff

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Mobile Robot ◽

Obstacle Avoidance ◽

Network Methods ◽

Artificial Neural

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

Multidisciplinary Aspects of Production Engineering ◽

10.2478/mape-2018-0026 ◽

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.

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