Fuzzy modeling and control based on maximum entropy self-organizing nets and cell state mapping

A method for the systematic design of a fuzzy model is developed for the control of complex systems. The proposed fuzzy controller design is based on a maximum entropy self-organizing net (MESON) and the cell state mapping approach. For fuzzy model identification, we present an approach to constructing a self-organizing fuzzy identifier. The proposed identifier is built on a neuro-fuzzy system consisting of a maximum entropy self-organizing net and a radial basis function network. We develop the corresponding self-organizing algorithms. To design a fuzzy controller, the proposed method combines the concept of cell state mapping with the synthesis techniques of MESON used in the fuzzy model identification.