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
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
TM
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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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 con-
strains read from the previously opened pattern file.
As a result the user will be supplied with a rule file
containing the linguistic expression of the rules. An
unsupervised clustering algorithm is used to per-
form 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 let the
fuzzy system to better approximate the proc-
ess/function sampling by means of subsequently
run sessions. Back-propagation algorithm with
automatic learning rate control is used to this aim.
TOOLS
It is composed of different sub-menus:
LOCAL RULES it allows to add new rules to the
fuzzy logic knowledge base determined by an
Adaptive Fuzzy Modeller run session. Aim of this
functionality is the local approximation level im-
provement.
SIMULATION it allows to simulate the fuzzy system
behaviour in order to verify the approximation level
obtained during the learning phase. The simulation
can be carried out in two different ways.
Simulation Step-by-Step: the user must supply
the simulator with the values variables correspond-
ing 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.
VIEW FEATURES
View Features of the AFM gives with the capability
to visualize the fuzzy model extracted for a particu-
lar project. It allows a separate visualization of the
rules of inference and membership functions. The
rules can be visualized in a linguistic format. For
the membership functions you can choose
between a linguistic and a graphical format
visualization.
EXPORTERS
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 program-
ming environment.
These functions can be exploited inside the user's
programs in order to verify the model extracted and
to use it in real application.
SUPPORTED TARGETS
The supported environment are:
- W.A.R.P.1.1 using FUZZYSTUDIO
TM
1.0
- W.A.R.P.2.0 using FUZZYSTUDIO
TM
2.0
- MATLAB
- C Language
- Fu.L.L. (Fuzzy Logic Language).
Learning
Phases
pattern file
Fuzzy Logic
knowledge base
Simulation
and Manual
Tuning
exporter to
processor
W.A.R.P. 1.1
W.A.R.P. 2.0
ANSI C
MATLAB
Rules
extractor
MFs
tuning
rules
minimizer
Figure 2. AFM Logic Flow.
Figure 3. BUILD MEMBERSHIP FUNCTION
window
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ADAPTIVE FUZZY MODELLER 1.0
SYSTEM REQUIREMENTS
MS-DOS version 3.1or higher
Microsoft Windows 3.0 or compatible later version
486, PENTIUM compatible processor chip
8 MBytes RAM (16 Mbytes recommended)
Hard Disk with at least 1MBytes free space
Order Code
Description
Supported Target
Functionalities
System Requirement
STFLAFM10/SW
WTA-FAM for Building Rules
BACK-FAM for Building MFs
STFLWARP11/PG
STFLWARP11/PL
STFLWARP20/PL
ANSI C
MATLAB
Rules Minimizer
Step-by-Step Simulation
Simulation from File
Local Tuning
MS-DOS 3.1or higher
Windows 3.0 or later
486, PENTIUM compatible
8 MB RAM
Type
Description
Operating Temperature
Package
STFLWARP11/PG-PL
HCMOS, 6KBytes RAM, 40MHz,
16 Inputs, 16 Outputs, 256 Rules
0 - 70C
CPGA100-PLCC84
STFLWARP20/PL
HCMOS, 1.4KBytes RAM, 40MHz,
8 Inputs, 4 Outputs, 256 Rules
0 - 70C
PLCC68
Type
Device
Development Tools
FUZZYSTUDIO
TM
TM
ADB
FUZZYSTUDIO
TM
TM
SDT
STFLSTUDIO10/KIT
STFLWARP11/PG
W.A.R.P. 1.X
W.A.R.P. 1.X programmer
EPROM programmer
RS-232 communication handler
Internal Clock
Variables and Rules Editor
W.A.R.P. Compiler/Debugger
Exporter for ANSI C and MATLAB
STFLSTUDIO2/KIT
STFLWARP20/PL
W.A.R.P.2.0
W.A.R.P.2.0 programmer
ZEROPOWER programmer
RS-232 communication handler
Internal Clock
Variables and Rules Editor
W.A.R.P. Compiler/Debugger
Exporter for ANSI C and MATLAB
3/4
ADAPTIVE FUZZY MODELLER 1.0
Information furnished is believed to be accurate and reliable. However, SGS-THOMSON Microelectronics assumes no responsibility for the
consequences of use of such information nor for any infringement of patents or other rights of third parties which may result from its use. No
license is granted by implication or otherwise under any patent or patent rights of SGS-THOMSON Microelectronics. Specification mentioned
in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied.
SGS-THOMSON Microelectronics products are not authorized for use as critical components in life support devices or systems without express
written approval of SGS-THOMSON Microelectronics.
1996 SGS-THOMSON Microelectronics Printed in Italy All Rights Reserved
FUZZYSTUDIO
TM
is a trademark of SGS-THOMSON Microelectronics
MS-DOS
, Microsoft
and Microsoft Windows
are registered trademarks of Microsoft Corporation.
MATLAB
is a registered trademark of Mathworks Inc.
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ADAPTIVE FUZZY MODELLER 1.0
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