Datasheet AFM 1.0 Datasheet (ST)

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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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SYSTEM REQ UIRE ME NTS
MS-DOS version 3.1or higher Microsoft Windows 3.0 or compatible later versio n 486, PENTIUM compati bl e processor chip 8 MBytes RAM (16 Mbytes recommended) Hard Disk with at least 1MBytes f ree space
ADAPTIVE FUZZY MOD ELL ER 1.0
Order Code Description Supported Target Functionalities System Requirement
STFLAFM10/SW
Type Description Operating Temperature Package
STFLWARP11/PG-PL
STFLWARP20/PL
Type Device
STFLSTUDIO10/KIT STFLWARP11/PG
STFLSTUDIO2/KIT STFLWARP20/PL
WT A -FAM for Bui l di n g Ru l es BACK-FAM for Building MFs
HCMOS, 6KBytes RAM, 40MHz, 16 Inputs, 16 Outputs, 256 Rules
HCMOS, 1.4KBytes RAM, 40MHz,
8 Inputs, 4 Outputs, 256 Rules
STFLWARP11/PG STFLWARP11/PL STFLWARP20/PL ANSI C MATLAB
®
FUZZYSTU DIO  ADB FUZZ YST U D IO SDT
W. A.R.P. 1.X W. A.R.P. 1.X programmer EPROM programmer RS-232 communication handler Internal Clock
W.A.R.P.2.0 W.A.R.P.2.0 programmer ZEROPOWER programmer RS-232 communication handler Internal Clock
Rules Minimizer Step-by-S tep Si mu lati on Simulation from File Local Tuning
0 - 70° C CPGA100-PLCC84
0 - 70°C PLCC68
Devel o p m en t Tools
Va r iables and Rules Editor W.A.R.P. Compiler/Debugger Exporter for ANSI C and MATLAB
Va r iables and Rules Editor W.A.R.P. Compiler/Debugger Exporter for ANSI C and MATLAB
MS-DOS 3.1or higher Windows 3.0 or later 486, PENTIUM compatible 8 MB RAM
®
®
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ADAPTIVE FUZZY MOD ELL ER 1.0
Information fur nishe d is be lie ve d to be a ccura te and relia ble . Ho wev er, SGS-THOM SON Microelec tronics assu mes 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 implicati on or ot h erwise under any patent or patent rights of SGS-THOMSON Microelectronics. Specification menti on ed in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied. SGS-THOMSON Microelectronics products ar e not authorized for use as critic al 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 is a trademark of SGS-THOMSON Microelectronics
®
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, Microsoft® and Microsoft Windows® are regist ere d tra dema rks of Mi croso ft C orpo ratio n.
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MATLAB
®
is a registered trademark of Mathworks Inc.
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