This paper presents an approach to build a soft sensor based on computational intelligence techniques. The goal is to identify fuzzy models from numerical data. First of all, the fuzzy model input variables are selected from the secondary variables set by applying Kohonen maps. Then, the Lipschitz quotients are used to select the lag structure of the fuzzy model. A fuzzy clustering algorithm is...

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results...

The problem of sparse fuzzy rule bases is introduced. Because of the high computational complexity of the original compositional rule of inference (CRI) method, it is strongly suggested that the number of rules in a final fuzzy knowledge base is drastically reduced. Various methods of analogical reasoning available in the literature are reviewed. The mapping style interpretation of fuzzy rules ...

Among the computational intelligence techniques employed to solve classification problems, the fuzzy rule-based classification system (FRBCS) is a popular tool capable of building a linguistic model interpretable to users. However, it may face lack of accuracy in some complex applications, by the fact that the inflexibility of the concept of the linguistic variable imposes hard restrictions on ...

Several Fuzzy Rule Interpolation (FRI) techniques have limitations from the direct application point of view, for example their applicability is limited to the one dimensional case, or they can be defined only based on the two closest surrounding rules of the actual observation. This is the reason why relatively few FRI methods can be found among the practical fuzzy rule based applications. Wit...

This paper presents an automatic design method for fuzzy systems using genetic algorithms. A exible, compact coding scheme for the genetic representation of the fuzzy rule base is suggested. The method is applied to adapt the behaviour of a mobile robot implemented by means of a fuzzy logic controller. The mobile robot is tested on real world situations.

Although Mamdani-type fuzzy rule-based systems (FRBSs) became successfully performing clearly interpretable fuzzy models, they still have some lacks related to their accuracy when solving complex problems. A variant of these kinds of systems, which allows to perform a more accurate model representation, are the so-called approximate FRBSs. This alternative representation still cannot avoid the ...

For constructing compact fuzzy rule-based systems with high classification performance, we have already formulated a rule selection problem. Our rule selection problem has two objectives: to minimize the number of selected fuzzy if-then rules (i.e., to minimize the fuzzy rule base) and to maximize the number of correctly classified patterns (i.e., to maximize the classification performance). In...

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact ...

A concept called the decomposition of multivariable control rules is presented. Fuzzy control is the application of the compositional rule of inference and it is shown how the inference of the rule base with complex rules can be reduced to the inference of a number of rule bases with simple rules. A fuzzy logic based controller is applied to a simple magnetic suspension system. The controller h...