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Designing Antecedent Membership Functions. Recommend designer to adopt the following design principles: Each Membership function overlaps only with the closest neighboring membership functions;

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Designing antecedent membership functions
Designing Antecedent Membership Functions

  • Recommend designer to adopt the following design principles:

    • Each Membership function overlaps only with the closest neighboring membership functions;

    • For any possible input data, its membership values in all relevant fuzzy sets should sum to 1 (or nearly)

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Designing antecedent membership functions1
Designing Antecedent Membership Functions

A Membership Function Design that violates the second principle

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Designing antecedent membership functions2
Designing Antecedent Membership Functions

A Membership Function Design that violates both principle

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Designing antecedent membership functions3
Designing Antecedent Membership Functions

A symmetric Function Design Following the guidelines

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Designing antecedent membership functions4
Designing Antecedent Membership Functions

An asymmetric Function Design Following the guidelines

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example furnace temperature control
Example: Furnace Temperature Control

  • Inputs

    • Temperature reading from sensor

    • Furnace Setting

  • Output

    • Power control to motor

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Matlab create membership functions temp
MATLAB: Create membership functions - Temp

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Matlab create membership functions setting
MATLAB: Create membership functions - Setting

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Matlab create membership functions power
MATLAB: Create membership functions - Power

* Fuzzy Systems Toolbox, M. Beale and H Demuth


If then rules
If - then - Rules

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Antecedent table
Antecedent Table

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Antecedent table1
Antecedent Table

  • MATLAB

    • A = table(1:5,1:3);

      • Table generates matrix represents a table of all possible combinations

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Consequence matrix
Consequence Matrix

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Evaluating rules with function frule
Evaluating Rules with Function FRULE

* Fuzzy Systems Toolbox, M. Beale and H Demuth


Design guideline inference
Design Guideline (Inference)

  • Recommend

    • Max-Min (Clipping) Inference method be used together with the MAX aggregation operator and the MIN AND method

    • Max-Product (Scaling) Inference method be used together with the SUM aggregation operator and the PRODUCT AND method

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example fully automatic washing machine
Example: Fully Automatic Washing Machine

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example fully automatic washing machine1
Example: Fully Automatic Washing Machine

  • Inputs

    • Laundry Softness

    • Laundry Quantity

  • Outputs

    • Washing Cycle

    • Washing Time

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example input membership functions
Example: Input Membership functions

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example output membership functions
Example: Output Membership functions

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example fuzzy rules for washing cycle
Example: Fuzzy Rules for Washing Cycle

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example control surface view clipping
Example: Control Surface View (Clipping)

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example control surface view scaling
Example: Control Surface View (Scaling)

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example control surface view
Example: Control Surface View

Scaling

Clipping

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example rule view clipping
Example: Rule View (Clipping)

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


Example rule view scaling
Example: Rule View (Scaling)

* Fuzzy Logic: Intelligence, control, and Information, J. Yen and R. Langari, Prentice Hall


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