A neural network approach to response surface methodology

Abstract

Response Surface Methodology is concerned with estimating a surface to a typically small set of observations with the purpose of determining what levels of the independent variables maximize the response. This usually entails fitting a quadratic regression function to the available data and calculating the function's derivatives.

Artificial Neural Networks are information-processing paradigms inspired by the way the human brain processes information. They are known to be universal function approximators under certain general conditions. This ability to approximate functions to any desired degree of accuracy makes them an attractive tool for use in a Response Surface analysis. This paper presents Artificial Neural Networks as a tool for Response Surface Methodology and demonstrates their use empirically.

Keywords:

• function approximation
• optimization

Additional information

Notes on contributors

Sandy D. Balkin