AFM 1.0
ADAPTIVE FUZZY MODELLER
ADVANCED DATA
Up to 8 Input Variables and 4 Output Variables
Up to 8 Fuzzy Sets for each Input Variables
Up to 2
14
Fuzzy 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
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 sam-
pling of a process/function expressed in terms of
Input\Output 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 rep-
resents an optimized approximation of the proc-
ess/function provided as input.
The AFM has the capability to translate its project
files to FUZZYSTUDIO鈩?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.
Figure 1. Block Diagram
May 1996
This is advance information on a new product now in development or undergoing evaluation. Details are subject to change without notice.
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