ASAFES2: a novel, neuro-fuzzy architecture for fuzzy computing, based on functional reasoning

The functional reasoning or Sugeno's fuzzy reasoning method is a very promising approach to the problem of non-linear, multi-variable, real valued function approximation, and the design of a fuzzy model and fuzzy controller. The proposed architecture, ASAFES2, is a function approximator which is, as far as we know, the first algorithm in the literature up to now that combines this particular reasoning method with stochastic reinforcement learning a class of quite powerful neural network training algorithms. It is a simple and versatile mathematical tool for fuzzy computing, featuring smooth and quick convergence and ease of use. The main ideas are the fuzzy partitioning of the input space into fuzzy subspaces (each corresponding to a possible fuzzy rule), and the use of a separate, stochastic reinforcement learning neural unit (ANASA II [1]) for every fuzzy subspace, in order to calculate the optimum consequence parameters. Some preliminary results are presented, proving ASAFES2 superior over back-propagation. A new, and "flexible" membership function is also proposed.