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Abstract

Open Access Biostatistics & Bioinformatics

Radial Basis Function Network versus Regression Model in Manufacturing Processes Prediction

  • Open or Close Homero De Jesus De Leon Delgado*

    University of the Valley of Mexico, México

    *Corresponding author: Homero De Jesus De Leon Delgado, University of the Valley of Mexico, Calle Tezcatlipoca 2301, Los Rodríguez, 25204 Saltillo, Coah, México, Tel: (+52)01 844 438 03 70; Email: homero.deleon@uvmnet.edu

Submission: February 01, 2018; Published: February 23, 2018

DOI: 10.31031/OABB.2018.01.000508

ISSN: 2578-0247
Volume1 Issue2

Abstract

One of the objectives of manufacturing industry, is to increase the efficiency in their processes using different methodologies, such as statistical modeling, for production control and decision-making. However, the classical tools sometimes have difficulty to depict the manufacturing processes. This paper is a comparative study between a multiple regression model and a Radial Basis Function Neural Network in terms of the statistical metrics R2 and R2 adj applied in a permanent mold casting process and TIG welding process. Results showed that in both cases, the RBF network performed better than Regression model.

Keywords: Radial basis function; Multiple regression; Process prediction

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