Improvements and critique on Sugeno's and Yasukawa's qualitative modeling

Investigates Sugeno's and Yasukawa's (1993) qualitative fuzzy modeling approach. We propose some easily implementable solutions for the unclear details of the original paper, such as trapezoid approximation of membership functions, rule creation from sample data points, and selection of important variables. We further suggest an improved parameter identification algorithm to be applied instead of the original one. These details are crucial concerning the method's performance as it is shown in a comparative analysis and helps to improve the accuracy of the built-up model. Finally, we propose a possible further rule base reduction which can be applied successfully in certain cases. This improvement reduces the time requirement of the method by up to 16% in our experiments

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

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

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

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

Notes on Sugeno and Yasukawa's fuzzy modelling approach

This paper investigates the Sugeno's and Yasukawa's qualitative fuzzy modelling approach. We propose some easily implementable solution for the unclear details of the original paper. These details are crucial concerning the method's performanc

Guest editorial: uncertainty modelling and intelligent information processing

In recent years, the availability of data and the ability to generate data in digital format have been observed to be growing tremendously. The Internet, manufacturing, design, biology, medical, business, financial, logistic and many other areas are experiencing data that increases in an exponential rate

Efficient fuzzy cognitive modeling for unstructured information

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

Feature selection for clustering based fuzzy modeling

In this paper, we propose a fast feature selection technique for clustering-based fuzzy modeling. The technique involves the creation of 'rough' fuzzy systems quickly from a set of data and chooses the one with the lowest error. The set of features used by the chosen fuzzy system is accepted as the optimal set of features. The effectiveness and efficiency of the proposed technique is validated using artificially generated data

A survey on universal approximation and its limits in soft computing techniques

This paper deals with the approximation behaviour of soft computing techniques. First, we give a survey of the results of universal approximation theorems achieved so far in various soft computing areas, mainly in fuzzy control and neural networks. We point out that these techniques have common approximation behaviour in the sense that an arbitrary function of a certain set of functions (usually the set of continuous function, C) can be approximated with arbitrary accuracy ε on a compact domain. The drawback of these results is that one needs unbounded numbers of “building blocks” (i.e. fuzzy sets or hidden neurons) to achieve the prescribed ε accuracy. If the number of building blocks is restricted, it is proved for some fuzzy systems that the universal approximation property is lost, moreover, the set of controllers with bounded number of rules is nowhere dense in the set of continuous functions. Therefore it is reasonable to make a trade-off between accuracy and the number of the building blocks, by determining the functional relationship between them. We survey this topic by showing the results achieved so far, and its inherent limitations. We point out that approximation rates, or constructive proofs can only be given if some characteristic of smoothness is known about the approximated function