university of regina faculty of engineering w i s e lab a cascaded fuzzy inference system for...

UNIVERSITY OF REGINAUNIVERSITY OF REGINAFACULTY OF ENGINEERINGFACULTY OF ENGINEERING

W I S E LABW I S E LAB

A Cascaded Fuzzy Inference System for A Cascaded Fuzzy Inference System for Dynamic Online Portals CustomizationDynamic Online Portals Customization

Erika Martinez RamirezErika Martinez RamirezDr. Rene V. MayorgaDr. Rene V. Mayorga

20032003

IntroductionIntroduction

• An application of Electronic Commerce using Fuzzy Logic Inference system is presented in this final project.

• Implementation of a Paradigm for Intelligent Decision and Control; and also the Intelligent Design and Operation of Human-Computer interfaces [1].

• The artificial technique used is Fuzzy Logic Inference and the software required execute the program is Matlab V.6.1.

Analize User Characteristics

Fuzzy Logic Inference

User Profile .

DevelopmentDevelopment

Purchaser Capacity

Purchaser Free Time

F U Z Z Y S Y S T E M

“ Main2 “

(Fuzzy Agent)

Preference

Link

(Fuzzy Agent)

Occupation

Purchaser Link

Occupation Experience

Gender

Age

Marital Status

Studies

Years WorkingChildren AgeOccupation

News

Health

Entertainment

Shopping

Travel

Preferences

News

Health

Others Links

DevelopmentDevelopment

Main2.fis Main2.fis

(General links Fuzzy Agent)(General links Fuzzy Agent)

PreferencesLinks.fis PreferencesLinks.fis

(Additional links Fuzzy Agent)(Additional links Fuzzy Agent)

TypeType MamdaniMamdani MamdaniMamdani

Number of Inputs Number of Inputs 77 77

Number of OutputsNumber of Outputs 55 44

Number of RulesNumber of Rules 5656 3434

Defuzzification MethodDefuzzification Method mommom mommom

Fuzzy System Characteristics

There are two Types of Fuzzy systems:

Mamdani-type inference -- A type of fuzzy inference in which the fuzzy sets from the consequent of each rule are combined through the aggregation operator and the resulting fuzzy set is defuzzified to yield the output of the system.

Sugeno-type inference -- A type of fuzzy inference in which the consequent of each rule is a linear combination of the inputs. The output is a weighted linear combination of the consequents.

The several defuzzification strategies are:

centroid : centroid of area methodbisector : bisector of area methodmom : mean of maximum methodsom : smallest of maximum methodlom : largest of maximum method

DevelopmentDevelopment

Years WorkingStudiesOccupation

Main2(Fuzzy Agent)

Rules

Purchaser Link

Rules

Rules

Rules

Purchaser Capacity

Purchaser Free Time

Occupation Experience

RulesOccupation

GenderAgeMarital Status

StudiesYears Working

Marital StatusChildren Age

Occupation

The relationship between inputs and outputs are as follow:

Purchase CapacityAge

EntertainmentShoppingTravel

Preference Link(Fuzzy Agent)

Rules

Preferences

Rules

Rules

Rules

News

Health

Others Links

AgeNews

AgeHealth

Inputs

Gender Age Studies Years Working Marital Status Occupational Area Children Age

MaleFemale

Very youngYoungMiddle ageOld

None

Primary-High-Tech School

University

Graduate Studies

NoneFewSomeSeveral

SingleMarried

EngineeringBusinessArtsSciences

LowMediumHigh

Outputs

Purchaser Capacity Purchaser Links Free Time Occupational Experience Occupation

Very poorPoorModerateGoodVery good

KidsTeensWomenManMature

NoneA littleMediumA lot

BeginnersModerateExperts

Technical-Engineering

Business-Administration-Marketing

Art-Music-Ballet

Medical-Computer-Biology

Input Variable: Gender

Input Variable: Age

Input Variable: Studies

Input Variable: Years Working

Input Variable: Marital Status

Input Variable: Children Age

Input Variable: Occupation

I N P U T S

O U T P U T SOutput Variable: Purchaser Capacity

Output Variable: Purchaser Links

Output Variable: Purchaser Free Time

Output Variable: Experience Level

Output Variable: Occupation

Age & Gender Purchaser Link

ManFemale Marital Status

Very Young Kids Kids --

Young Teen Teen --

Middle age Men Woman Single BoySingle Girls

Old Mature Mature Single Mature

Rules

Purchaser LinkGenderAgeMarital Status

RulesPurchaser CapacityStudies

Years Working

Years working & Level of education Purchaser Capacity

NoneElementary High schoolTech School

University Graduate Studies

None Very Poor Very Poor Very Poor Very Poor

Few Poor Moderate Good Good

Some Poor Good Good Very Good

Several Moderate Good Very Good Very Good

Occupation & Level of education Experience

NoneElementary High schoolTech School

UniversityGraduate Studies

Engineering Beginners Beginners Experts Experts

Business Intermediate Intermediate Experts Experts

Arts Beginners Beginners Intermediate Experts

Sciences Beginners Intermediate Intermediate Experts

Gender & Children Age Purchaser Free Time

NoneBabies Kids Teenagers

Single A lot None A little Moderate

Married Moderate A little Moderate A lot

RulesPurchaser Free TimeMarital Status

Children Age

Years WorkingStudiesOccupation

Rules Occupation Experience

Purchase CapacityAge

EntertainmentShoppingTravel

Preference Link(Fuzzy Agent)

Rules

Rules

Rules

Rules

AgeNews

AgeHealth

Preferences

News

Health

Others Links

Preference Link

Fuzzy Agent

I N P U T S

Input Variable: Purchaser Capacity

Input Variable: Age

Input Variable: Entertainment

Input Variable: Shopping

Input Variable: Travel

Input Variable: News

Input Variable: Health

O U T P U T SOutput Variable: Preferences

Output Variable: News

Output Variable: Health

Output Variable: Others Links

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