On design of flexible neuro-fuzzy systems for nonlinear modelling

Abstract

In this work, we consider the flexible neuro-fuzzy systems of the Mamdani-type. When designing such systems to solve approximation problem, we should choose triangular norms used in inference and aggregation operators. This can be done by trial and error. In this work, we propose an algorithm that allows in an automatic way to choose the types of triangular norms in the learning process. The task of this algorithm is also an automatic selection of parameters of all functions describing the system. The algorithm uses an evolutionary strategy for its action and has been tested using well-known approximation benchmarks.

Keywords:

• fuzzy logic
• neuro-fuzzy systems
• evolutionary strategy (μ,λ);
• nonlinear modeling

Acknowledgments

The authors would like to thank the referees for their suggestions and comments. This paper was prepared under the project operated within the Foundation for Polish Science Team Program co-financed by the EU European Regional Development Fund, Operational Program Innovative Economy 2007–2013, and also financed by the National Science Center on the basis of the decision number DEC-2012/05/B/ST7/02138.