
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
1.0
- W.A.R.P.2.0 using FUZZYSTUDIO
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