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Industrial Application of Fuzzy Logic Control

Tutorial and Workshop

© Constantin von Altrock

Inform Software Corporation

2001 Midwest Rd.

Oak Brook, IL 60521, U.S.A.

German Version Available!

Phone 630-268-7550

Fax 630-268-7554

Email: fuzzy@informusa.com

Internet: www.fuzzytech.com

Fuzzy Logic Development Methodology According to ISO/IEC Standards

- Relevant Standards for Fuzzy Logic
- Future Standards for Fuzzy Logic
- General Development Methodology- Goals- Phase Plan- Support with Fuzzy Software Tools
- Specific Development Methodology- Goals- Design Decisions- Fuzzy Design Wizards

© INFORM 1990-1998 Slide 1

Fuzzy Logic:- Relevant Standards -

ISO 9000 International Quality Standard

- Design Documentation
- Design Modifications Documentation
- Testing Procedure Documentation

IEC 1131+ Industrial Automation Standard

- Data Exchange Formats for Portability
- Integration with Conventional Techniques
- Development Methodology

Both General and Specific Standards for Fuzzy Logic Systems Development Exist !

© INFORM 1990-1998 Slide 2

Fuzzy Logic:- Future Standards -

IEEE Standard Specific Standards for Fuzzy Logic:

- Terminology and Algorithms(Different words for the same things and same words for different things are confusing for practitioners)
- Universal Programming Language for Fuzzy Logic
- Meaningful Benchmarks (Real-world oriented benchmarks for platform selection and comparison)
- Further Refined Development Methodology
- Fuzzy-”Plug-Ins” for Standard Applications
- Adaptation Techniques for Fuzzy Logic Systems

Under Construction !

© INFORM 1990-1998 Slide 3

General Fuzzy Logic Development Methodology

- Goals of the General Fuzzy Logic Development Methodology:
- Definition of Non-Ambiguous and Transparent Design Steps
- Definition of Minimum Criteria for Project Structuring, Reporting, and Documentation (Final System, Revision, and Design Steps)

Validation Complies with ISO 9000 !

- The Results of This Are:
- A Complete and Transparent Coverage of the Entire Development Process
- Raised Awareness for Design Step Decision Criteria and Their Consequences
- Non-Ambiguous Mapping of External Services in a Complete Development Project
- Protection from Liability Claims
- Unfortunately, Also More Effort…

© INFORM 1990-1998 Slide 4

B-Report

Acceptance

Project Start

General Fuzzy Logic Development Methodology

Phase Plan:

The General Fuzzy Logic Development Methodology Structures the Complete Project !

Preliminary Evaluation

A-Audit

Prototype

B-Audit

Off-Line Optimization

Setup

Optimization

Documentation

Audit = Capture of Process Knowledge and Experience from Operators and Engineers

Report = Protocol of an Audit Result

© INFORM 1990-1998 Slide 5

General Fuzzy Logic Development Methodology

Preliminary Evaluation

Assessment As to Whether Fuzzy Logic Is Applicable for the Given Application

Problem Analysis Before Project Start !

- Evaluation Criteria:
- Has Fuzzy Logic Been Previously Applied to a Similar Application With Success?
- Is It a Multi-Variable Type Control Problem?
- Do Operators and Engineers Possess Knowledge About Any Relevant Interdependencies of the Process Variables?
- Can Further Knowledge About the Process Behavior Be Gained By Observation Or Experiments?
- Is It Difficult to Obtain a Mathematical Model from the Process?

© INFORM 1990-1998 Slide 6

General Fuzzy Logic Development Methodology

Systematic Representation of the Available Process Knowledge !

A-Audit

- Preparation:
- Auditors: Familiarization with the Domain of the Application, Definition of a Specific Questionnaire
- Plant Operators: Documentation of the Existing Measurement and Control Systems, Description of All Relevant Sensors and Actuators of the Process -- Including Min/Max Values and Tolerances, and Provision of Trend Charts Which Display Typical Behavior
- Action:
- Analysis of the Quality Variables and Criteria Variables
- Analysis of the Command Variables
- Analysis of the Current Performance
- A-Report:
- Written Summary of the Audit by the Auditors, Review and Acceptance by the Plant Operator

© INFORM 1990-1998 Slide 7

General Fuzzy Logic Development Methodology

Prototype

- Implementation of a Prototype Based on the A-Report:
- As a Demonstration for the B-Audit
- As a Starting Point for the Next Development Step
- Prototype Generation According to the Specific Fuzzy Development Methodology:
- Definition of the System Structure
- Creation of the Vocabulary
- Formulation of a First Rule Base

Rapid Application Development !

© INFORM 1990-1998 Slide 8

General Fuzzy Logic Development Methodology

B-Audit

Revision of the Prototype !

- Preparation:
- Auditors: Preparation of the Prototype as a Demonstration, Creation of a Questionnaire Comprising All Open Issues
- Action:
- Joint Discussions About Inconsistencies and Missing Parts of the A-Audit , Plus Further In-Depth Discussion about all Unclear Issues Still Remaining
- Revision of the A-Report to the B-Report
- Definition of Procedures for a Safe Setup of the Controller with Respect to not Disturbing the Running Process and Endangering Process Safety
- B-Report:
- Written Summary of the Audit by the Auditors, Plus Review and Acceptance by the Plant Operator

© INFORM 1990-1998 Slide 9

General Fuzzy Logic Development Methodology

Off-Line Optimization

- Extension and Refinement of the Prototype Based on the Results of the B-Report:
- Revision of the Linguistic Variable Definitions
- Revision and Extension of the Rule Base
- Verification By:
- Off-Line Analysis of the Partial Transfer Characteristics
- Use of Existing Software Simulations of the Plant
- Use of Existing Recorded Process Data for Testing

Implementation of Operation Knowledge !

© INFORM 1990-1998 Slide 10

General Fuzzy Logic Development Methodology

Setup

Online Optimization

- Setup of the Fuzzy Logic Controller:
- Implementation on the Target Hardware and Online-Link to the Development PC
- Integration with Existing Control System and Implementation of Pre-/Postprocessing
- Creation of Safety Function (Limits, Manual Operation Switch, Safe State,..)
- Online Optimization of the Fuzzy Logic Controller:
- “Open-Loop” Operation to Validate the Controller’s Behavior
- “Supervised” Operation for Fine-Tuning of Rules and Membership Functions
- “What-If” Analyses to Optimize Control Performance

Verification with the Running Process !

© INFORM 1990-1998 Slide 11

General Fuzzy Logic Development Methodology

Documentation

- Documentation of the Final Design of the Fuzzy Logic Controller:
- Structure of Fuzzy Logic System
- Vocabulary of Fuzzy Logic System
- Rule Bases of Fuzzy Logic System
- Documentation of the Development Process:
- Audit Reports
- Final Reports
- Development History of the Fuzzy Logic System

Using Fuzzy Logic Development Tools Drastically Reduces Paperwork Expenditures !

© INFORM 1990-1998 Slide 12

General Fuzzy Logic Development Methodology

- fuzzyTECH Software Development Tools for Fuzzy Logic Systems Support the Process By:
- Including Local Documentation for All Objects of a Fuzzy Logic System
- Automatic Generation of Complete System and History Documentation
- An Embedded Revision Control System

The Inter-Operation of these Components Reduces the Documentation Effort by 75 - 95% !

© INFORM 1990-1998 Slide 13

General Fuzzy Logic Development Methodology

Ability to Include Comments About Any Object of a Design Within the Development Software:

Transparent View of the Comments During Development:

Definition of Comments:

The Documentation Evolves DURING Development !

© INFORM 1990-1998 Slide 14

General Fuzzy Logic Development Methodology

Automated Generation of Complete System Documentation:

- Export, Modify, and Print in Word Processor
- Automatic Integration into Plant Operation Manual
- Documentation in Multiple Languages

Complete Documentation at Any Level of Development in Just Seconds !

© INFORM 1990-1998 Slide 15

General Fuzzy Logic Development Methodology

Integrated Revision Control System:

- Complete Development History in a Single File
- Documentation of all Levels of Development
- Protection Against Unauthorized Access

Access the Entire Development History at Any Time !

© INFORM 1990-1998 Slide 16

Specific Fuzzy Logic Development Methodology

- Goals of the Specific Fuzzy Logic Development Methodology:
- Definition of a Clear and Non-Ambiguous Design Approach to all Components and Objects of a Fuzzy Logic System (Linguistic Variables, Rules, Structure,…)
- Definition of the Involved Criteria for the Design Decisions

Validation Complies with ISO 9000 !

- The Results of This Are:
- “Cookbook-Recipe”-Type Definition with Respect to Real-World Needs
- Shorter Initial Training Period for Fuzzy Logic Designers
- Avoidance of Misunderstandings and Errors
- Protection Against Unsound Liability Claims
- Future Expansion or Modification of the System Without Risks

© INFORM 1990-1998 Slide 17

Specific Fuzzy Logic Development Methodology

Design Steps:

Design Methods

Design Decisions:

- Structural Analysis
- Definition ofVocabulary
- Definition ofInter-Dependencies
- Verification

- Expert Audit
- Offline Simulation
- Offline Plausibility Testing of the Rule Blocks
- Offline Test Using Process Data
- Online Optimization

- Structural Definition
- Type of Membership Function
- Inference Methods
- Operator Choice
- Choice of Defuzzification Method

?

?

The Specific Fuzzy Logic Development Methodology Structures the Actual Fuzzy System Design !

© INFORM 1990-1998 Slide 18

- The Individual Design Decisions Are Defined by Their Design Criteria and Consequences for the Final System Behavior
- Each Design Step Involves Its Individual Methodology and Design Decisions
- “Sanity-Checks” Are Conducted After Each Step
- Compliance with the Procedures Can Be Checked (It Is “Certifiable”)
- The Development Path of a Fuzzy Logic System Developed Is Transparent and Reproducible, Even to Others

Structure

Linguistic Variables

Fuzzy Rules

Offline Test

Setup

Well-Defined Design Approach Rather Than “Trial-and-Error” !

Maintenance

© INFORM 1990-1998 Slide 19

Input Variables

Connections

Defuzzification

Definition of

Systems Structure

- Output Variables: What Types of Decisions Must the Fuzzy Logic System Make (0/1, inc/dec, absolute)?
- Input Variables: Which Are Available from the Process, and Which Shall Be Used First?
- Connections: Which Input Variables Influence What Output Variables? Are Intermediate Aggregations Possible?
- Defuzzification: “Best Compromise” or “Most Plausible Solution”?

Structure

Linguistic Variables

Fuzzy Rules

Offline Test

Setup

Maintenance

The First Development Step Defines the Outline of the Fuzzy Logic System !

© INFORM 1990-1998 Slide 20

Input Variables

Connections

Defuzzification

Systems Structure

- Output Variables -

- What Types of Decisions Shall the Fuzzy Logic System Make:
- Absolute Values to Actuators
- Absolute Values As Set Points to Underlying Controllers
- Relative Values to Modify the Set Point Value of Underlying Controllers (increment/decrement)
- Discrete Decisions (on/off, ...)

- Documentation of the Output Variables:
- What Is the Influence of Each Output Variable in the Process?
- What Is the Interval in which the Output Variable Shall Be Varied, and Which “Typical” Values Exist?
- Are There “Safe State” Values?

Exact Definition of the Expected Outputs !

© INFORM 1990-1998 Slide 21

Input Variables

Connections

Defuzzification

Systems Structure

- Input Variables -

- Documentation of All Available Input Variables:
- Which Aspect of the Process Are Described by the Input Variable?
- What Is the Value Interval of the Input Variable? Which “Typical” Values Exist?
- What Are the Tolerances of the Sensors, and How Accurate Is the Measured Information?
- What Is the Time Delay of the Input Variable?

- Which of the Available Input Variables Shall Be Used:
- For Each Output Variable, Define a List of the Its Influencing Input Variables, Sorted by Relative Importance
- From This List, Identify the Smallest Set of Input Variables That Suffice to Control All Output Variables

Inventory of All Available Input Variables!

© INFORM 1990-1998 Slide 22

Input Variables

Connections

Defuzzification

Systems Structure

- Connections -

- Identification of Interdependencies in the Decision Structure:
- For Each Output Variable, Which Input Variables Are Influencing It?
- Can Meaningful Intermediate Variables that Describe Process States Be Defined?

Simple Structure = Complex Rule Definitions

The More a Decision Can Be Structured, the More Trans-parent the Resulting System Will Be !

Complex Structure = Simple Rule Definitions

© INFORM 1990-1998 Slide 23

Input Variables

Connections

1

v_high

high

med

low

#1

µ

Defuzzification

#2

0

CH4

#2

1

#1

on

off

µ

0

Fire

Systems Structure

- Defuzzification -

- Definition of the Defuzzification Method for Each Output Variable:
- For Continuous Variables: “Best Compromise“ := CoM
- For Discrete Variables: “Most Plausible Result” := MoM

#1: IF Temp = high OR Press = high THEN CH4 = low (0.6)

#2: IF Temp = med AND Press = med THEN CH4 = med (0.2)

CoM

#1: IF Temp = high AND Flow = ok THEN Fire = on (0.8)

#2: IF Temp = med AND Flow = low THEN Fire = off (0.9)

The Use of the Output Variable in the Control System Determines the Defuzzification Method !

MoM

© INFORM 1990-1998 Slide 24

System Structure

The Fuzzy Design Wizard in fuzzyTECH:

Output Variables

Input Variables

Connections

Defuzzification

Embedded “Fuzzy-Expert”!

© INFORM 1990-1998 Slide 25

Type of Memb.Fct.

Membership Fct.

Definition of Linguistic Variables

- How Many Terms Should Be Defined for Each Linguistic Variable?
- Which Type of Membership Functions Should Be Used for the Variables?
- How Can Plausible Membership Functions for the Terms Be Defined?

Structure

LinguisticVariables

Fuzzy Rules

Offline Test

Setup

The Second Design Step Defines the Vocabulary of the Fuzzy Logic System !

Maintenance

© INFORM 1990-1998 Slide 26

Type of Memb.Fct.

Membership Fct.

Linguistic Variables- Number of Terms -

- Heuristic Method (“Cookbook Recipe”):
- Nearly All Linguistic Variables Have Between 3 and 7 Terms
- Most Often, the Number of Terms Is an Odd Number
- ... Hence the Number of Terms Is Either 3, 5, or 7

- Practical Approach:
- Initial “Test”-Rules Indicate the Number of Terms Necessary
- A Rule-of-Thumb: Start With 3 Terms for Each Input Variable and 5 Terms for Each Output Variable

Start With A Minimum Number of Terms, Since New Terms Can Be Added As Needed !

© INFORM 1990-1998 Slide 27

Type of Memb.Fct.

Membership Fct.

Linguistic Variables- Membership Function Types -

Empirical Psycho Linguistic Research Has Shown that Membership Function Definitions Should Obey the Following Axioms:

1. µ(x) continuous over X

2. µ‘(x) continuous over X

3. µ‘‘(x) continuous over X

4. µ: minµ{maxx{µ‘‘(x)}} for all X

Cubic Interpolative Spline Functions Satisfy These Axioms:

For Most Real-World Applications, a Linear Approximation Suffices !

© INFORM 1990-1998 Slide 28

Type of Memb.Fct.

Membership Fct.

Example of Linguistic Variable “Error”:

1

µ

0

-10

-5

0

+5

+10

Error

Linguistic Variables- Membership Functions -

Definition in Four Easy Steps:

1. For Each Term, Define a Typical Value/Interval

2. Define µ=1 for This Value/Interval

3. Define µ=0 from Which the Next Neighbor is µ=1

4. Join Points With Linear / Cubic Spline Functions

large_p: 10positive: 3

zero: 0

negative: -3

large_n: -10

ONE Typical Value Per Linguistic Term Suffices for Definition of Membership Functions !

© INFORM 1990-1998 Slide 29

Type of Memb.Fct.

Membership Fct.

Example of Linguistic Variable “Error”:

1

µ

0

-10

-5

0

+5

+10

Error

Linguistic Variables- Membership Functions -

Definition in Four Easy Steps:

1. For Each Term, Define a Typical Value/Interval

2. Define µ=1 for This Value/Interval

3. Define µ=0 from Which the Next Neighbor is µ=1

4. Join Points With Linear / Cubic Spline Functions

large_p: 10positive: 3

zero: [-1;1]

negative: -3

large_n: -10

A “Typical Value” May Also Be an Interval !

© INFORM 1990-1998 Slide 30

Type of Memb.Fct.

Membership Fct.

Linguistic Variables- Membership Functions -

Structured Definition of Linguistic Variables in fuzzyTECH:

Definition of Complete Sets of Membership Functions in One Easy Step !

© INFORM 1990-1998 Slide 31

Result Agg. Op.

Definition of Rules

Definition of the

Fuzzy Rules Base

- Which Fuzzy Logic Operator for the Rule Premise Aggregation Step?
- Which Fuzzy Logic Operator for the Rule Result Aggregation Step?
- How Are the Actual Fuzzy Logic Rules Defined?

Structure

Linguistic Variables

Fuzzy Rules

Offline Test

Setup

The Third Design Step Defines the Actual Control Strategy !

Maintenance

© INFORM 1990-1998 Slide 32

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Aggregation Operator -

- Elementary Fuzzy Logic Operators:
- AND: µAvB = min{ µA; µB }
- OR: µA+B = max{ µA; µB }
- NOT: µ-A = 1 - µA

- ...Model Human Evaluation and Reasoning Poorly Sometimes
- Example: IF Car=fast AND Car=economical THEN Car=good
- Car 1: 180km/h: µ=0.3 9l/100km: µ=0.4 -> 0.3
- Car 2: 180km/h: µ=0.3 7l/100km: µ=0.6 -> 0.3
- Car 3: 175km/h: µ=0.25 4l/100km: µ=0.9 -> 0.25

The Exclusive Use of Elementary Fuzzy Logic Operators Can Inflate the Rule Base !

Mock-Up Solution: Define More Fuzzy Rules:

pretty_fast

highly_economic

pretty_good

IF Car=fast AND Car=economical THEN Car=good

somewhat_fast

mildly_economic

just_ok

© INFORM 1990-1998 Slide 33

Result Agg. Op.

Definition of Rules

MIN

MAX

AND

OR

Definition of the Fuzzy Rules

- Aggregation Operator -

Transfer Characeristics of MIN and MAX:

Compensatory Operators Better Represent Human Evaluation and Reasoning:

In Most Real-World Applications, MIN and MAX Are Sufficient !

The Gamma-Operator Can Be Tuned:

© INFORM 1990-1998 Slide 34

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Aggregation Operator -

fuzzyTECH Uses Parametric Fuzzy Operators:

Transparent Parameterization Through Instant Visualization of Transfer Characteristics !

© INFORM 1990-1998 Slide 35

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Result Agg. Operator -

- Two Methods Are Applied in Real-World Applications:
- “The Winner Takes It All” (MAX)
- “One Man, One Vote” (BSUM)

Rules:

#1: ... => Power = high (0.3)

#2: ... => Power = med (0.1)

#3: ... => Power = med (0.4)

#4: ... => Power = med (0.6)

#5: ... => Power = low (0.0)

MAX:

med (0.6)

BSUM:

med (1.0)

If the Rule Base Is Not Symmetrical, BSUM Can Yield Wrong Results !

© INFORM 1990-1998 Slide 36

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Definition of Rules -

- Basic Properties of Rule Bases:
- Normalization (all Brackets Resolved)
- Elementation (only “AND” Operators Used)

Example of a Non-Normalized Non-Elementary Rule:

IF (((Press_1 = low AND Press_2 = low) OR (Press_3 = med AND NOT Temp_2 = high)) AND (Press_1 = low OR Temp_1 = high)) THEN CH4 = med

- Different Rule Block Definition Approaches:
- Induction: Define the “THEN”-Part for All Possible Input Term Combinations (only with 2..3 Input Variable per Rule Block)
- Deduction: Define Rules As Single Pieces of Experience (Prefer “Thin” Rules)
- Linear Approach: Stepwise Optimization of a “Linear” Fuzzy Rule Base (Mostly Used with “Direct” Fuzzy Controllers)

The Experience to Be Implemented Determines the Procedure!

© INFORM 1990-1998 Slide 37

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Definition of Rules -

- fuzzyTECH Supports All Three Approaches:
- Automatic Generation of Inductive Fuzzy Rule Bases

Definition of a Consequence for Every Possible Situation !

© INFORM 1990-1998 Slide 38

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Definition of Rules -

- fuzzyTECH Supports All Three Approaches:
- Optimized Editors and Analyzers for Deductive Rule Definition (Table, Text, and Matrix Type Representation)

Each Rule Expresses an Aspect of the Experience !

© INFORM 1990-1998 Slide 39

Result Agg. Op.

Definition of Rules

Definition of the Fuzzy Rules

- Definition of Rules -

- fuzzyTECH Supports All Three Approaches:
- Fuzzy Rule Wizard for Automated Generation of Linear Rule Blocks

Complete Rule Block Definition in One Step !

© INFORM 1990-1998 Slide 40

Process Simulation

Process Data Test

Off-Line Testing

- Which Fuzzy Rules Are Missing, Superfluous, or Conflicting?
- Fine-Tuning the Linguistic Variables With the Help of Process Simulation
- Optimization With Data From the Actual Process

Structure

Linguistic Variables

Fuzzy Rules

Off-Line Testing

Setup

In Off-Line Testing, the First Verification of the Fuzzy System Occurs !

Maintenance

© INFORM 1990-1998 Slide 41

Process Simulation

Process Data Test

Off-Line Testing

- Rule Validation 1 -

Analysis Tools in fuzzyTECH for Rule Validation:

Direct Analysis of the Data Range in a 3D Graph !

© INFORM 1990-1998 Slide 42

Process Simulation

Process Data Test

Off-Line Testing

- Rule Validation 2 -

Analysis Tools in fuzzyTECH for Rule Validation:

Verification of Individual Rule Blocks With the Statistics Analyzer !

© INFORM 1990-1998 Slide 43

Process Simulation

Process Data Test

Off-Line Testing

- Process Simulation 1 -

- Dynamic Links in fuzzyTECH to Simulation Tools and Programming Languages:
- Fuzzy Control Blocks in VisSim™, Matlab/SIMULINK™, ...
- Standard Links Like DDE, DLL, OLE, ActiveX, Data, ...
- You Can Tie in the Editors and Analyzers of fuzzyTECH With Your Own Software
- Either Complete fuzzyTECH (With All Editors and Analyzers) or Runtime Module (Highest Performance) Can Be Used

Open Links Allow Connection With Most Any Software !

© INFORM 1990-1998 Slide 44

Process Simulation

Process Data Test

Off-Line Testing

- Process Simulation 2 -

Dynamic Monitoring and Tuning in fuzzyTECH:

Asynchronous Coupling of Simulation and fuzzyTECH !

© INFORM 1990-1998 Slide 45

Process Simulation

Process Data Test

Off-Line Testing

- Process Data Test -

Dynamic Optimization Using Actual Process Data in fuzzyTECH:

Verification of the Entire Fuzzy Controller in Real Process Situations !

© INFORM 1990-1998 Slide 46

Warm Operation

Hot Operation

Setup

- Implementation of the Fuzzy Controller on the Target Hardware
- Implementation of the Online Process Link
- Warm Operation = Output of the Fuzzy Controller Is Not Switched Through to the Process
- Hot Operation = Output of the Fuzzy System Is Switched Through to the Process

Structure

Linguistic Variables

Fuzzy Rules

Offline Test

Setup

Final Validation of the Complete Fuzzy System in Online Operation !

Maintenance

© INFORM 1990-1998 Slide 47

Setup- Implementation on Target -

- Various Implementation Techniques Available in fuzzyTECH:
- Embedded Control: Assembly Code Kernels
- Industrial Automation: Fuzzy Function Blocks for PLCs
- Process Supervisory Control: Fuzzy Modules for DCS, SCADA..
- Universal: Common Source Code Output (C, C++, VB, Pascal, ..)

Online Link Between the Process Hardware (Target) and fuzzyTECH:

For Most Industrial Target Platforms, an Optimized Implementation Technique Exists !

© INFORM 1990-1998 Slide 48

- Analysis of Behavior Over Time in fuzzyTECH:
- Dynamic in All Editors and Analyzers
- Special Behavior Over Time of Variables, Terms and Rules in a Time Plot

Real-Time Remote Debugging for Systems Verification !

© INFORM 1990-1998 Slide 49

Monitoring

Review

Operation and Maintenance

- Final Documentation of the Fuzzy Logic Design and Its Integration (Comprises All Previous Design Steps Documentation)
- Configuration of the Monitor Component for the Supervision of Fuzzy Logic System Performance
- Optional Review of Fuzzy Logic Design and Modifications of the System As Result of Review

Structure

Linguistic Variables

Fuzzy Rules

Offline Test

Setup

Development Methodology Continues During Operation !

Maintenance

© INFORM 1990-1998 Slide 50

- fuzzyTECH Supports These Steps Through:
- Documentation: Documentation Generator and Revision Control System
- Remote Tracing for Online Monitoring

Unsupervised Monitoring Using Trigger Conditions !

- Review of the Linguistic Variables and Fuzzy Rules Through “What-If” Analyses

© INFORM 1990-1998 Slide 51

An Easily Reproduced, Certifiable Process to Reach Your Solution !

Summary of Specific Fuzzy Development Methodology

1. Structure Definition

1.1 Documentation of All Output Variables

1.2 Documentation of All Input Variables

1.3 Structuring of the Decision (“many small rule blocks”)

1.4 Defuzzification Method Selection (“Best Compromise“ or “Most Plausible Solution”?)

2. Linguistic Variables

2.1 Number of Terms per Variable (start with 3 per input and 5 per output variable)

2.2 Type of Membership Function (start with Standard-MBFs)

2.3 Membership Function Definition by Standard Method (typical values => MBF’s)

3. Fuzzy Rule Definition

3.1 Fuzzy Operator for Aggregation (start with MIN)

3.2 Fuzzy Operator for Result Aggregation (start with MAX)

3.3 Select Rule Definition Approach Depending on Application (Inductive, Deductive, Linear)

4. Offline Testing

4.1 Validation of the Rule Blocks (identification of missing and conflicting rules)

4.2 Testing Using Process Simulation (if available)

4.3 Testing Using Real Process Data (if available)

5. Setup

6. Operation and Maintenance

© INFORM 1990-1998 Slide 52

fuzzyTECH Supports Entire Fuzzy Design Methodology

The Optimal Tool for Every Step of the Design Process !

Structure

=> Fuzzy Design Wizard

Linguistic Variables

=> Linguistic Variable Wizard

Fuzzy Rules

=> Fuzzy Rule Wizard, Rule Block Utilities

Offline Test

=> Offline Debug Mode, Analyzer

Setup

=> Online Debug Mode, Analyzer

Maintenance

=> Trace, Documentation Generator, Revision Control System

© INFORM 1990-1998 Slide 53

“Hands-On” Guides for Fuzzy Logic Systems Development:

"Fuzzy Logic and NeuroFuzzy Applications Explained" by Constantin von Altrock, $39.95, Prentice Hall, ISBN 0-1336-8465-2, shows how to design technical fuzzy logic applications and contains a demo of the fuzzyTECH software with 10 control system simulations to experiment with. It also explains the use of fuzzy and NeuroFuzzy techniques in over 70 case studies.

"[This book] ... is packed with information which is presented in a reader-friendly fashion. It is a must for anyone who is interested in the analysis and design of fuzzy logic based systems.” Lotfi Zadeh, Berkeley

"Fuzzy Logic and NeuroFuzzy Applications in Business and Finance" by Constantin von Altrock, $39.95, Prentice Hall 1996, ISBN 0-13-591512-0, shows how to design business and finance fuzzy logic applications and contains a demo of the fuzzyTECH for Business software with numerous case studies to experiment with. It also explains the use of fuzzy and NeuroFuzzy techniques in recent successful practical implementations.

"... We owe to Constantin v. Altrock our thanks and congratulations for authoring an innovative text that is leading the way. Fuzzy Logic and NeuroFuzzy in Business and Finance is must reading for anyone who has a serious interest in adding new tools to the armamentarium of decision analysis ..." Lotfi Zadeh, Berkeley

© INFORM 1990-1998 Slide 54

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