Kok Wai Wong

7404759276

Publications - 5

Guest editorial: Uncertainty modelling and intelligent information processing

L. T. Koczy Kok Wai Wong

Publication Name: Memetic Computing

Publication Date: 2010-12-01

Volume: 2

Issue: 4

Page Range: 247-248

Description:

No description provided

Open Access: Yes

DOI: 10.1007/s12293-010-0052-5

Fuzzy signature and cognitive modelling for complex decision model

T. D. Gedeon L. T. Koczy Kok Wai Wong

Publication Name: Advances in Soft Computing

Publication Date: 2007-12-01

Volume: 42

Issue: Unknown

Page Range: 380-389

Description:

As data is getting more complex and complicated, it is increasingly difficult to construct an effective complex decision model. Two very obvious examples where such a need emerges are in the economic and the medical fields. This paper presents the fuzzy signature and cognitive modeling approach which could improve such decision models. Fuzzy signatures are introduced to handle complex structured data and problems with interdependent features. A fuzzy signature can also be used in cases where data is missing. The proposed fuzzy signature structure will be used in problems that fall into this category. This paper also investigates a novel cognitive model to extend the usage of fuzzy signatures. This Fuzzy Cognitive Signature Modelling will enhance the usability of fuzzy theory in modelling complex systems as well as facilitating complex decision making process based on ill structured information or data. © 2007 Springer-Verlag Berlin Heidelberg.

Open Access: Yes

DOI: 10.1007/978-3-540-72434-6_38

Efficient fuzzy cognitive modeling for unstructured information

T. D. Gedeon L. T. Koczy Kok Wai Wong

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2006-12-01

Volume: Unknown

Issue: Unknown

Page Range: 358-363

Description:

This paper presents an efficient fuzzy cognitive modeling which can handle granulation, organisation and causation. This cognitive modeling technique consists of multiple levels where the lowest level includes details required to make a decision or to transfer to the next stage. This Fuzzy Cognitive Modeling will enhance the usability of fuzzy theory in modeling complex systems as well as facilitating complex decision making process based on ill structured or missing information or data. © 2006 IEEE.

Open Access: Yes

DOI: 10.1109/FUZZY.2006.1681737

Fuzzy rule interpolation for multidimensional input spaces with applications: A case study

T. D. Gedeon Domonkos Tikk L. T. Koczy Kok Wai Wong

Publication Name: IEEE Transactions on Fuzzy Systems

Publication Date: 2005-12-01

Volume: 13

Issue: 6

Page Range: 809-819

Description:

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications. © 2005 IEEE.

Open Access: Yes

DOI: 10.1109/TFUZZ.2005.859316

Construction of fuzzy signature from data: An example of S ARS pre-clinical diagnosis system

T. D. Gedeon L. T. Koczy Kok Wai Wong

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2004-12-01

Volume: 3

Issue: Unknown

Page Range: 1649-1654

Description:

There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. Fuzzy signatures are introduced to handle complex structured data and interdependent feature problems. Fuzzy signatures can also used in cases where data is missing. This paper presents the concept of a fuzzy signature and how its flexibility can be used to quickly construct a medical pre-clinical diagnosis system. A Severe Acute Respiratory Syndrome (SARS) pre-clinical diagnosis system using fuzzy signatures is constructed as an example to show many advantages of the fuzzy signature. With the use of this fuzzy signature structure, complex decision models in the medical field should be able to be constructed more effectively.

Open Access: Yes

DOI: 10.1109/FUZZY.2004.1375428