Creating Fuzzy Models from Limited Data

Abstract

The design of experiments is a methodological approach in which measurement experiments are carefully planned to obtain highly informative data. This paper addresses the challenge of constructing mathematical models for complex nonlinear processes when the available measurement data have low information content. This problem often arises when data are collected without the guidance of an experimental modeling expert. We examine two practical examples to illustrate this issue: a textile wastewater decolorization process and atmospheric corrosion of structural metal materials. In both cases, the measured data were insufficient to construct highly accurate models. It is, therefore, necessary to make a trade-off between model complexity and accuracy by adapting modeling techniques to work effectively with the limited data available. The main aim of the paper is, therefore, to focus on simple but effective techniques that allow as much information as possible to be extracted from low-quality measurements and to maximize the usefulness of the model for its intended purpose.