STFLWARP20L [STMICROELECTRONICS]

ADAPTIVE FUZZY MODELLER; 自适应模糊MODELLER


型号：	STFLWARP20L
厂家：	ST
描述：	ADAPTIVE FUZZY MODELLER 自适应模糊MODELLER
文件：	总4页 (文件大小：92K)
中文：	中文翻译
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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 2¹⁴Fuzzy Rules

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).

Rules Learning Phase using an unsupervised

WTA-FAM

Membership Functions Learning Phase using

a supervised BACK-FAM

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.

Automatic and Manual Learning Rate

Rules Minimizer

Gaussian and Triangular Membership

Functions Shape

The AFM has the capability to translate its project

files to FUZZYSTUDIO project files, MATLAB

and C code, in order to use this environment as a

support for simulation and control .

Inference method based on Product or Minimum

Step-by-Step and from File Simulation available

The block diagram in fig.2 illustrates the AFM logic

flow.

Supported Target: W.A.R.P. 1.1, W.A.R.P. 2.0,

MATLAB and ANSI C

Figure 1. Block Diagram

May 1996

1/4

This is advance information on a new product now in development or undergoing evaluation. Details are subject to change without notice.

ADAPTIVE FUZZY MODELLER 1.0

LEARNING

Figure 2. AFM Logic Flow.

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 readfromthepreviously openedpatternfile.

pattern file

rules

minimizer

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.

Fuzzy Logic

knowledge base

Learning

Phases

exporter to

processor

BUILDING MEMBERSHIP FUNCTIONS It allows

the user to select the membership function shape

and the fuzzy intference method for the project

elaboration.

Rules

extractor

Simulation

MFs

W.A.R.P. 1.1

W.A.R.P. 2.0

ANSI C

and Manual

tuning

Tuning

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.

MATLAB

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.

Figure 3. BUILD MEMBERSHIP FUNCTION

window

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.

TOOLS

It is composed of different sub-menus:

SUPPORTED TARGETS

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.

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

SIMULATIONitallows tosimulatethefuzzy system

behaviour in order to verify the approximation level

obtained during the learning phase. The simulation

can be carried out in two different ways.

- C Language

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

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.

2/4

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

STFLWARP11/PG

STFLWARP11/PL

STFLWARP20/PL

ANSI C

Rules Minimizer

MS-DOS 3.1or higher

Windows 3.0 or later

486, PENTIUM compatible

8 MB RAM

Step-by-Step Simulation

Simulation from File

Local Tuning

WTA-FAM for Building Rules

BACK-FAM for Building MFs

STFLAFM10/SW

MATLAB^®

Type

Description

Operating Temperature

Package

HCMOS, 6KBytes RAM, 40MHz,

16 Inputs, 16 Outputs, 256 Rules

STFLWARP11/PG-PL

0 - 70°C

CPGA100-PLCC84

HCMOS, 1.4KBytes RAM, 40MHz,

8 Inputs, 4 Outputs, 256 Rules

STFLWARP20/PL

0 - 70°C

PLCC68

Development Tools

Type

Device

FUZZYSTUDIO ADB

FUZZYSTUDIO SDT

W.A.R.P. 1.X

W.A.R.P. 1.X programmer

STFLWARP11/PG EPROM programmer

RS-232 communication handler

Internal Clock

Variables and Rules Editor

STFLSTUDIO10/KIT

W.A.R.P. Compiler/Debugger

Exporter for ANSI C and MATLAB^®

W.A.R.P.2.0

W.A.R.P.2.0 programmer

Variables and Rules Editor

STFLSTUDIO2/KIT

STFLWARP20/PL

ZEROPOWER programmer

RS-232 communication handler

Internal Clock

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.

FUZZYSTUDIO 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.

SGS-THOMSON Microelectronics GROUP OF COMPANIES

Australia - Brazil - Canada - China - France - Germany - Hong Kong - Italy - Japan - Korea - Malaysia - Malta - Morocco - The Netherlands -

Singapore - Spain - Sweden - Switzerland - Taiwan - Thailand - United Kingdom - U.S.A.

4/4

STFLWARP20L [STMICROELECTRONICS]

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