About Bioline  All Journals  Testimonials  Membership  News

Journal of Applied Sciences and Environmental Management
World Bank assisted National Agricultural Research Project (NARP) - University of Port Harcourt
ISSN: 1119-8362
Vol. 22, No. 6, 2018, pp. 875-881
Bioline Code: ja18150
Full paper language: English
Document type: Research Article
Document available free of charge

Journal of Applied Sciences and Environmental Management, Vol. 22, No. 6, 2018, pp. 875-881

 en Modeling Performance of Response Surface Methodology and Artificial Neural Network


In recent years, response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering. Both RSM and ANN strategies have particular computational properties that makes them suitable for making predictions, but differ in their extrapolation and interpolation capabilities on complex non-linear processes, and thus potentially conflict in their predictive accuracy. This study models and compares the capabilities of RSM and ANN in predicting the tensile strength of a 6 mm thick mild steel gas tungsten arc welded plate based on the effects of input variables such as weld current, weld speed, gas flow rate and filler rod. The RSM and ANN based models for prediction were compared using the coefficient of determination criteria. With a higher value of 0.836, the ANN model proved to be a better modeling technique than the RSM model.

Soft Computing Techniques; Response Surface Method; Artificial Neural Network

© Copyright 2018 - Sada

Home Faq Resources Email Bioline
© Bioline International, 1989 - 2024, Site last up-dated on 01-Sep-2022.
Site created and maintained by the Reference Center on Environmental Information, CRIA, Brazil
System hosted by the Google Cloud Platform, GCP, Brazil