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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:

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

DOI: 10.31031/OABB.2018.01.000508

ISSN: 2578-0247
Volume1 Issue2


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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