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Industrial Application of Fuzzy Logic Control

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 .

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Industrial Application of Fuzzy Logic Control

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  1. 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

  2. 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

  3. 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

  4. 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

  5. A-Report 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. Design Step Overview • 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

  20. Output Variables 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

  21. Output Variables 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

  22. Output Variables 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

  23. Output Variables 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

  24. Output Variables 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

  25. Definition of System Structure The Fuzzy Design Wizard in fuzzyTECH: Output Variables Input Variables Connections Defuzzification Embedded “Fuzzy-Expert”! © INFORM 1990-1998 Slide 25

  26. Number of Terms 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

  27. Number of Terms 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

  28. Number of Terms 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

  29. Number of Terms 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

  30. Number of Terms 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

  31. Number of Terms 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

  32. Aggregation Op. 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

  33. Aggregation Op. 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

  34. Aggregation Op. 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

  35. Aggregation Op. 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

  36. Aggregation Op. 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

  37. Aggregation Op. 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

  38. Aggregation Op. 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

  39. Aggregation Op. 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

  40. Aggregation Op. 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

  41. Rule Validation 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

  42. Rule Validation 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

  43. Rule Validation 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

  44. Rule Validation 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

  45. Rule Validation 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

  46. Rule Validation 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

  47. Implementation 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

  48. 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

  49. Setup: Warm/Hot Operation • 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

  50. Documentation 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

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