SGS Thomson Microelectronics STFLAFM10-SW Datasheet
AFM 1.0
ADAPTIVE FUZZY MODELLER
ADVANCED DATA
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
Automati c 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
May 1996
This is advance information on a new product now in development or undergoing evaluation. Details are subject to change without notice.
DESCRIP TION
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 provi ded a s input.
The AFM has the capability to translate its project
files to FU ZZY 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.
Figure 1. Blo ck Diag r a m
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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 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 rul e 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.
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 allo ws to simulate 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.
VIEW FEATURES
View Featur es of the AFM gi ves wi th 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 using FUZZ Y STUDI O 1.0
- W. A.R.P.2.0 using FUZZ Y STUDI O 2.0
- MA T LAB
- 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 MOD ELL ER 1.0
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