An automatic method to generate fuzzy rules and their membership functions to recognize handwritten characters is described. Firstly an initial rule base is created on the basis of a referential data set containing handwriting prototypes. Subsequently the classification behavior of the fuzzy rules is optimized with a genetic algorithm, which is regarded as a typical solution to NP-complete prob...

Computing with words (CWW) relies on linguistic representation of knowledge that is processed by operating at the semantical level defined through fuzzy sets. Linguistic representation of knowledge is a major issue when fuzzy rule based models are acquired from data by some form of empirical learning. Indeed, these models are often requested to exhibit interpretability, which is normally evalua...

Over the last years, a number of methods have been proposed to automatically learn and optimize fuzzy rule bases from data. The obtained rule bases are usually robust and allow an interpretation even for data sets that contains imprecise or uncertain information. However, most of the proposed methods are still restricted to learn and/or optimize single layer feed-forward rule bases. The main di...

Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem. mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment. i.e., lack ...

fuzzy controllers are being used in various control schemes. the aim of this work was adjusting hemodialysis machine parameters utilizing a fuzzy logic controller (flc) so that patient’s hemodynamic condition remains stable during hemodialysis treatment. for this purpose a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and...

F TP (Fuzzy Template Predicate) is proposed as a template to incorporate concept-based match into fuzzy production languages. A thesaurus augmented in F TP supports the concept-based match, which is more sophisticated than previous fuzzy match mechanisms. Membership functions for fuzzy linguistic variables and fuzzy numbers are used as an interface to the thesaurus. F TP also has self reening f...

Abstruct-Fuzzy rule-base modeling is the task of identifying the structure and the parameters of a fuzzy IF-THEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to fine-tune membership functions in a fuzzy rule base has received more and more attention. In this paper we summarize Jang’s architecture of employing an adaptive network and the Kalman ...

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given no...

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation...

In this paper we present a new learning method for rule-based feed-forward and recurrent fuzzy systems. Recurrent fuzzy systems have hidden fuzzy variables and can approximate the temporal relation embedded in dynamic processes of unknown order. The learning method is universal i.e. it selects optimal width and position of Gaussian like membership functions and it selects a minimal set of fuzzy...