Context-based fuzzy system for optimization

Presents an approach to the development of an empirical fuzzy system for optimization, which is based on a contextual fuzzy filtering technique. The proposed system is based not on fuzzy rules, but rather on a set of empirical data to describe the functional dependence between the inputs and outputs. A detailed description of the fuzzy system including its structure and its main components (context variables, input and output membership functions) is provided and compared with conventional rule-based fuzzy systems. Additionally, a numerical example is also presented to illustrate the implementation of the proposed technique. The proposed approach is proven to be robust to uncertainties and has the ability to handle non-linearity.