designing antecedent membership functions n.
Download
Skip this Video
Download Presentation
Designing Antecedent Membership Functions

Loading in 2 Seconds...

play fullscreen
1 / 25

Designing Antecedent Membership Functions - PowerPoint PPT Presentation


  • 60 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Designing Antecedent Membership Functions' - jonah


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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