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

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

May 1996

This is advance information on a new product now in development or undergoing evaluation. Details are subject to change without notice.

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

™ 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

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