Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient
Andrzej Macioł, Piotr Macioł · AGH University of Science and Technology Press eBooks · 2021
The paper describes a solution of a problem of developing of fuzzy rules compliant with the Takagi-Sugeno approach where a number of available examples (observations) is not sufficient.Modeling of fuzzy premises and generating functions describing a dependence of a result variable on antecedents are described.In our original approach a problem of identification of membership functions of variables com-posing premises and a problem of consequent parameters identification are solved.For the first one, we used a simple technique based on individual judgements of experts.The second one is solved with a linear programming method.In particular, our approach to formulate the consequent parameter identification problem allows using of an extremely effective T-S method when a data-driven approach cannot be applied.In the paper we present a description of our methods and results of simulations of accuracy of the proposed approach, based on commonly known benchmarks.The achieved accuracy of classification is sufficient for the most of decision-making systems of an expert nature.