Universal fuzzy system representation with XML Authors ： Chris Tseng, Wafa Khamisy, Toan Vu Source ： Computer Standards & Interfaces, Volume 28, Issue 2, December 2005, Pages Speaker ：徐任鵬 Date ： 2006/01/12

2/31 Outline Introduction Extensible Markup Language-XML  Document Type Definition-DTD  XML Schema Fuzzy logic  Fuzzy system Fuzzy system in XML  Fuzzy system components  Fuzzy system data types  Fuzzy system schema Examples and application Conclusion

3/31 Introduction (1/3) In a complex system where there are ambiguity and vagueness, fuzzy logic can be an ideal methodology. Collaboration and subsequent integration with other fuzzy designs become a difficult issue. For complex fuzzy system design, it is essential that a standard platform for fuzzy logic data and structure sharing be available.

4/31 Introduction (2/3) One way to represent fuzzy system and its components is through the Extensible Markup Language (XML). We demonstrate how fuzzy systems described in XML with our proposed schema can be universally compatible with different software by using XSLT (eXtensible Style Language Transformation) stylesheets.

5/31 Introduction (3/3) The proposed schema offers a standard platform for fuzzy system developers and users to collaborate without compatibility issues.

6/31 Extensible Markup Language-XML XML is grammatical system for constructing custom markup languages. XML is platform independent and is becoming a standard to exchange data over the internet.

7/31 Document Type Definition-DTD A DTD defines the syntax of an XML language. tag is used to define all the legal elements allowed in the XML document. An element can have a list of attributes with the tag. Entities are declared within the DTD by the tag.

8/31 XML Schema An XML Schema Language is used to describe the structure of an XML document. XML Schema is a newer schema for XML as compared with DTD and has several advantages over DTD.

9/31 Fuzzy logic Fuzzy logic introduces a set of membership functions that maps between linguistic elements to numerical values in the context of fuzzy set. The fuzzy membership function value indicates the degree to which an element belong to a fuzzy set.

10/31 Fuzzy system (1/2) Fuzzy system are usually input/output systems with appropriate membership functions for some applications. Some successful fuzzy system applications ： anti-lock brake system 、 satellite tracking.

11/31 Fuzzy system (2/2) A fuzzy system usually consists of four components ：  Fuzzification Interface  Fuzzy Inference Engine  Fuzzy Rule Base  Defuzzification Interface

12/31 Fuzzy system in XML (1/2) We encapsulates fuzzy system description in common elements that can be used to represent any fuzzy system.

13/31 Fuzzy system in XML (2/2)

14/31 Fuzzy system components (1/2) The high level hierarchy of our fuzzy system has the following main components ：  Input Base ： It is a component that consists of a collection of inputs.  Membership Function Repository ： This component contains all the membership function used to describe the fuzzy system.  Inference Engine ： It is a component that defines all the operators used to perform inferencing.  Operator Repository ： This component contains all the operators used to describe the fuzzy system.

15/31 Fuzzy system components (2/2)  Rule Base ： It is a collection of fuzzy If-Then rules.  Defuzzification ： It is a fuzzy system component that translates fuzzy set output values into crisp values.  Output Base ： It is a component that consists of a collection of outputs.

16/31 Fuzzy system data types At the lower level, it has the following main data types ：  Linguistic Variable ： A linguistic variable has a range of values and at least one linguistic term.  Linguistic Term ： A linguistic term has a membership function.  Membership Function ： A membership function can be either a pre-defined function or a user-defined function.  Operator ： An operator can be either a pre-defined operator or a user-defined operator.  Rule ： A rule consists of at least one antecedent and one consequent.

17/31 Fuzzy system schema <xs:schema xmlns:xs=“ elementFormDefault=“qualified” attributeFormDefault=“unqualified”> Fuzzy system component,that captures all of the input variables Fig. 6. InputBase.xsd

18/31 <xs:schema xmlns:xs=“ elementFormDefault=“qualified” attributeFormDefault=“unqualified”> Fuzzy system and it’s components Fig. 7. FuzzySystem.xsd

19/31 We demonstrate how fuzzy systems in XML can be universally compatible with Matlab and FuzzyJess with appropriate XSLT stylesheet designs. Examples and application (1/12)

20/31 Examples and application (2/12) The general procedure is illustrated as following

21/31 Examples and application (3/12) The Tipper fuzzy system is designed to give advice on the amount of tip a person should give based on the quality of food and service.

22/31 Examples and application (4/12) Consider the tipper system with the following 3 rules ： TS-  Rule 1 ： If service is poor and food is rancid then tip is cheap.  Rule 2 ： If service is good then tip is average.  Rule 3 ： If service is excellent and food is delicious then tip is generous.

23/31 Examples and application (5/12) Fig. 10. Rule 2 of TipperSystem.xml

24/31 Examples and application (6/12)

25/31 Examples and application (7/12)

26/31 Examples and application (8/12)

27/31 Examples and application (9/12)

28/31 Examples and application (10/12)

29/31 Examples and application (11/12)

30/31 Examples and application (12/12)

31/31 Conclusion We presents an XML methodology to represent fuzzy systems for facilitating collaborations in fuzzy applications and design. Fuzzy system can be represented in different formats understood by different applications using the concept of XSLT stylesheets. With an example, we shows how can represent that given fuzzy system in XML and transform it to comprehensible formats for Matlab and FuzzyJess applications.