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UNIVERSITY OF REGINA FACULTY OF ENGINEERING W I S E LAB A Cascaded Fuzzy Inference System for Dynamic Online Portals Customization Erika Martinez Ramirez.

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Presentation on theme: "UNIVERSITY OF REGINA FACULTY OF ENGINEERING W I S E LAB A Cascaded Fuzzy Inference System for Dynamic Online Portals Customization Erika Martinez Ramirez."— Presentation transcript:

1 UNIVERSITY OF REGINA FACULTY OF ENGINEERING W I S E LAB A Cascaded Fuzzy Inference System for Dynamic Online Portals Customization Erika Martinez Ramirez Dr. Rene V. Mayorga 2003

2 Introduction 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.

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4 Development 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 Working Children Age Occupation News Health Entertainment Shopping Travel Preferences News Health Others Links

5 Development

6 Main2.fis (General links Fuzzy Agent) PreferencesLinks.fis (Additional links Fuzzy Agent) TypeMamdaniMamdani Number of Inputs 77 Number of Outputs 54 Number of Rules 5634 Defuzzification Method 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 method bisector:bisector of area method mom: mean of maximum method som: smallest of maximum method lom:largest of maximum method

7 Development Years Working Studies Occupation Main2 (Fuzzy Agent) Rules Purchaser Link Rules Purchaser Capacity Purchaser Free Time Occupation Experience Rules Occupation Gender Age Marital Status Studies Years Working Marital Status Children Age Occupation The relationship between inputs and outputs are as follow: Purchase Capacity Age Entertainment Shopping Travel Preference Link (Fuzzy Agent) Rules Preferences Rules News Health Others Links Age News Age Health

8 Inputs GenderAgeStudiesYears WorkingMarital StatusOccupational AreaChildren Age Male Female Very young Young Middle age Old None Primary-High-Tech School University Graduate Studies None Few Some Several Single Married Engineering Business Arts Sciences Low Medium High Outputs Purchaser CapacityPurchaser LinksFree TimeOccupational ExperienceOccupation Very poor Poor Moderate Good Very good Kids Teens Women Man Mature None A little Medium A lot Beginners Moderate Experts Technical-Engineering Business-Administration- Marketing Art-Music-Ballet Medical-Computer-Biology

9 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

10 O U T P U T S Output Variable: Purchaser Capacity Output Variable: Purchaser Links Output Variable: Purchaser Free Time Output Variable: Experience Level Output Variable: Occupation

11 Age & Gender  Purchaser Link Man FemaleMarital Status Very YoungKids -- YoungTeen -- Middle ageMenWomanSingle Boy Single Girls OldMature Single Mature Rules Purchaser Link Gender Age Marital Status Rules Purchaser Capacity Studies Years Working Years working & Level of education  Purchaser Capacity None Elementary High school Tech School UniversityGraduate Studies None Very Poor FewPoorModerateGood SomePoorGood Very Good SeveralModerateGoodVery Good

12 Occupation & Level of education  Experience None Elementary High school Tech School UniversityGraduate Studies Engineering Beginners Experts Business Intermediate Experts Arts Beginners IntermediateExperts Sciences Beginners Intermediate Experts Gender & Children Age  Purchaser Free Time None BabiesKidsTeenagers Single A lot NoneA littleModerate MarriedModerateA littleModerateA lot Rules Purchaser Free Time Marital Status Children Age Years Working Studies Occupation Rules Occupation Experience

13 Purchase Capacity Age Entertainment Shopping Travel Preference Link (Fuzzy Agent) Rules Age News Age Health Preferences News Health Others Links Preference Link Fuzzy Agent

14 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

15 O U T P U T S Output Variable: Preferences Output Variable: News Output Variable: Health Output Variable: Others Links

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18 Thanks !

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