DESCRIPTION
Adaptive Fuzzy Modeller (AFM) is a tool that easily allows to obtain a model of a system based on Fuzzy Logic data structure, starting from the sampling of a process/function expressed in terms of InputOutput values pairs (patterns).
Its primary capability is the automatic generation of a database containing the inference rules and the parameters describing the membership functions.
The generated Fuzzy Logic knowledge base represents an optimized approximation of the process/function provided as input.
The AFM has the capability to translate its project files to FUZZYSTUDIO project files, MATLAB and C code, in order to use this environment as a support for simulation and control .
The block diagram in fig.2 illustrates the AFM logic flow.

■ Up to 8 Input Variables and 4 Output Variables
■ Up to 8 Fuzzy Sets for each Input Variables
■ Up to 2¹⁴Fuzzy Rules
■ Rules Learning Phase using an unsupervised WTA-FAM
■ Membership Functions Learning Phase using a supervised BACK-FAM
■ Automatic and Manual Learning Rate
■ Rules Minimizer
■ Gaussian and Triangular Membership Functions Shape
■ Inference method based on Product or Minimum
■ Step-by-Step and from File Simulation available
■ Supported Target: W.A.R.P. 1.1, W.A.R.P. 2.0, MATLAB and ANSI C