ST AFM 1.0 User Manual

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AFM 1.0

ADAPTIVE FUZZY MODELLER

ADVANCED DA TA

Up to 8 Input Variabl es and 4 Output Variable s
Up to 8 Fuzzy Sets for each Input Variables
Up to 214 Fuzzy Rules
Rules Learnin g P hase using an un supervised

WTA-FAM
Membership Functions Learning Phase using

a supervised BACK-FAM
Autom atic 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,

MA TLAB and AN SI C

Figure 1. Blo ck Diag r a m

DESCRIPTION

Adaptive Fuz zy 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 t erms of
Input\Output values pairs (patterns).

Its primary c apability is the autom atic 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 proc­es s/fu n c ti o n provided as input.

The AFM has the capability to translate its project
files to FUZZY STUDIO project files, MATLAB
and C code, in order to use this environment a s a
support for simulation and control .

The block diagram in fig.2 illustrate s the AFM logic
flow.

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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ADAPTIVE FUZZY MOD ELL ER 1.0

LEARNING

it is composed by two phases:
BUILDING RULES It allows to perform the auto-

matic selection of inference rules or their manual
definition, taking in to account the project con­strai ns read f rom the prev iously opened pattern file.

As a result the user will be supplied with a rule file
containing the lingui stic ex pression of t he rule s. An
unsupervised clustering algorithm is used to per­form this task.

BUILDING MEMBERSHIP FUNCTIONS It allows
the user to select the membership function shape
and the fuzzy intference method for the project
elaboration.

Starting from the rule file supplied by the previous
phase, it initially associates to each fuzzy set a
standard membership function shape. These
shapes can be gradually tuned in order to le t the
fuzzy system to better approximate the proc­ess/function sampling by means of subsequently
run sessions. Back-propagation algorithm with
automatic learning rate control is used to this aim.

Figure 3. BUILD MEMBERSHIP FUNCTION
window

TOOLS

It is composed of different sub-menus:
LOCAL RULES it allows to add new r ules to the

fuzzy logic knowledge base determined by an
Adaptive Fuzzy Modeller run session. Aim of this
functionality is the local approximation level im­provement.

SIMULATION it allows to simulat e the fuzzy system
behaviour in order to verify the approxi mation level
obtained during the learni ng phase. The simulation
can be carried out in two different ways.

Simulation Step-by-Step: the user must supply
the simulator with the val ues variables correspond­ing to the point to verify.

Simulation from File: the user must supply the
simulator with the name of a process/function
stream file that will be used to perform a complete
process inference.

Figure 2. AFM Logic Flow.

pattern file

Learning

Phases

Rules

extractor

MFs

tuning

Fuzzy Logic

knowledge base

Simulation

and Manual

Tuning

rules

minimizer

exporter to

processor

W.A.R.P. 1.1
W.A.R.P. 2.0

ANSI C

MATLAB

VIEW FEATURES

View Featur es of the AFM gi ves w ith the capabi lity
to visualize th e fuzzy model extracted for a par ticu­lar project. It allows a separate visual iz ation of the
rules of inference and membership functions. The
rules can be visualized in a linguistic format. Fo r
the membership functions you can choose
between a linguistic and a graphica l format
visualization.

EXPOR TERS

The Exporter provides library functions working
on the databases automatically generated, which
appropriately describe the data structures of the
selected project in terms of a different program­ming environment.

These functions can be exploited inside the user’s
programs in order to verify the model extrac ted and
to use it in real application.

SUPPORTED TARGETS

The supported environment are:

- W. A.R.P.1.1 u sing FUZZ Y STUDI O 1.0

- W. A.R.P.2.0 u sing FUZZ Y STUDI O 2.0

- MA T LAB

- C Language

- Fu.L.L. (Fuzzy Logic Language).

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