Fuzzy Rule Interpolation Research Papers (original) (raw)

The concept of fuzzy rule interpolation in sparse rule bases was introduced in 1993. It has become a widely researched topic in recent years because of its unique merits in the topic of fuzzy rule base complexity reduction. The first... more

The concept of fuzzy rule interpolation in sparse rule bases was introduced in 1993. It has become a widely researched topic in recent years because of its unique merits in the topic of fuzzy rule base complexity reduction. The first implemented technique of fuzzy rule interpolation was termed as α-cut distance based fuzzy rule base interpolation. Despite its advantageous properties in various approximation aspects and in complexity reduction, it was shown that it has some essential deficiencies, for instance, it does not always result in immediately interpretable fuzzy membership functions. This fact inspired researchers to develop various kinds of fuzzy rule interpolation techniques in order to alleviate these deficiencies. This paper is an attempt into this direction. It proposes an interpolation methodology, whose key idea is based on the interpolation of relations instead of interpolating α-cut distances, and which offers a way to derive a family of interpolation methods capable of eliminating some typical deficiencies of fuzzy rule interpolation techniques. The proposed concept of interpolating relations is elaborated here using fuzzy- and semantic-relations. This paper presents numerical examples, in comparison with former approaches, to show the effectiveness of the proposed interpolation methodology.

In most fuzzy systems, the completeness of the fuzzy rule base is required to generate meaningful output when classical fuzzy reasoning methods are applied. This means, in other words, that the fuzzy rule base has to cover all possible... more

In most fuzzy systems, the completeness of the fuzzy rule base is required to generate meaningful output when classical fuzzy reasoning methods are applied. This means, in other words, that the fuzzy rule base has to cover all possible inputs. Regardless of the way of rule base construction, be it created by human experts or by an automated manner, often incomplete rule bases are generated. One simple solution to handle sparse fuzzy rule bases and to make infer reasonable output is the application of fuzzy rule interpolation (FRI) methods. In this paper, we present a Fuzzy Rule Interpolation Matlab Toolbox, which is freely available. With the introduction of this Matlab Toolbox, different FRI methods can be used for different real time applications, which have sparse or incomplete fuzzy rule base.

A major issue in the field of fuzzy applications is the complexity of the algorithms used. In order to obtain efficient methods, it is necessary to reduce complexity without losing the easy interpretability of the components. One of the... more

A major issue in the field of fuzzy applications is the complexity of the algorithms used. In order to obtain efficient methods, it is necessary to reduce complexity without losing the easy interpretability of the components. One of the possibilities to achieve complexity reduction is to combine fuzzy rule interpolation with the use of hierarchical structured fuzzy rule bases, as proposed by Sugeno et al. (1991). For interpolation, the method of Koczy and Hirota (1993) is used, but other techniques are also suggested. The difficulty of applying this method is that it is often impossible to determine a partition of any subspace of the original state space so that in all elements of the partition the number of variables can be locally reduced. Instead of this, a sparse fuzzy partition is searched for and so the local reduction of dimensions will be usually possible. In this case however, interpolation in the sparse partition itself, i.e. interpolation in the meta-rule level is necessary. This paper describes a method how such a multilevel interpolation is possible

Fuzzy rule interpolation-based reasoning methods are the most common choices for cases when the applied rule base is not dense. This paper presents a new techniquecalled LESFRI, which is based on the method of least squares. Its central... more

Fuzzy rule interpolation-based reasoning methods are the most common choices for cases when the applied rule base is not dense. This paper presents a new techniquecalled LESFRI, which is based on the method of least squares. Its central idea is theconservation of the shape type specific to a fuzzy partition. The method has lowcomputational complexity.
Keywords: fuzzy rule interpolation, LESFRI, sparse fuzzy rule base

This paper aims the introduction and comparison of two novel fuzzy system generation methods that implement theconcept of incremental Rule Base Extension (RBE). Bothmethods automatically obtain from given input-output data a lowcomplexity... more

This paper aims the introduction and comparison of two novel fuzzy system generation methods that implement theconcept of incremental Rule Base Extension (RBE). Bothmethods automatically obtain from given input-output data a lowcomplexity fuzzy system with a sparse rule base.
Keywords-rule base generation; sparse rule base; fuzzy ruleinterpolation; rule base extension

Fuzzy modeling has great adaptability to thevariations of system configuration and operation conditions. This paper investigates the fuzzy modeling of a laboratory scale system of anaerobic tapered fluidized bed reactor (ATFBR). The... more

Fuzzy modeling has great adaptability to thevariations of system configuration and operation conditions. This paper investigates the fuzzy modeling of a laboratory scale system of anaerobic tapered fluidized bed reactor (ATFBR). The studied system is the anaerobic digestion of synthetic wastewater derived from the starch processing industries. The experiment was carried out in a mesophilic ATFBR reactor with mesoporous granulated activated carbon as bacterial support.The fuzzy system was generated and trained by a modified version of the Projection based Rule Extraction (PRE) method using the obtained experimental data, and it applies the inference technique Fuzzy Rule Interpolation based on Polar Cuts (FRIPOC).The output parameters predicted by the tuned system have been found to be very close to the correspondingexperimental ones and the model was validated by replicative testing. Keywords: fuzzy modeling, FRIPOC, Anaerobic Tapered Fluidized bed Reactor, OLR, COD, BOD, pH

The aim of this paper is to introduce a novel two-step Fuzzy Rule InterpolationTechnique (FRIT) “VEIN”, based on the concept of Vague Environment. The strength of FRIT against classical fuzzy reasoning methods is the ability of gaining... more

The aim of this paper is to introduce a novel two-step Fuzzy Rule InterpolationTechnique (FRIT) “VEIN”, based on the concept of Vague Environment. The strength of FRIT against classical fuzzy reasoning methods is the ability of gaining conclusion even incase where the knowledge is represented by sparse fuzzy rule bases. The FRIT “VEIN”introduced in this paper is following the structure the Generalized Methodology of fuzzyrule interpolation [1], by adapting the concept of Vague Environment [4] for approximatedescription of fuzzy partitions [6].
Keywords: Vague Environment, Fuzzy Rule Interpolation, Fuzzy Set Interpolation, Single Rule Reasoning

One of the most critical steps during the developmentof a fuzzy system is the identification of the fuzzy rule base andthe fuzzy partitions, the so-called “tuning”. This paper intends topresent a comparative study of three different fuzzy... more

One of the most critical steps during the developmentof a fuzzy system is the identification of the fuzzy rule base andthe fuzzy partitions, the so-called “tuning”. This paper intends topresent a comparative study of three different fuzzy partitionparameter identification methods with respect to the effect of different fuzzy partition parameterization strategies.
Keywords-fuzzy system tuning; rule base optimization; sparse rule base; fuzzy rule interpolation

Humans make use of facial expression to communicate in their day to day interactions with each other, which comes naturally without much effort. Facial expression is essentially a communication and interaction between humans and where... more

Humans make use of facial expression to communicate in their day to day interactions with each other, which comes naturally without much effort. Facial expression is essentially a communication and interaction between humans and where other information like speech is not available; it becomes what one can depend on to transmit emotion or reactions of an individual. Hence, human expression recognition with high recognition is still an interesting task. This study is aimed at implementing face detection and expression recognition using fuzzy rule interpolation (FRI) technique. This follows through a development of specifications for fuzzy rule interpolation in emotion recognition using the viola jones algorithm as the detection algorithm and local binary pattern (LBP) algorithm for the feature extraction. The extended Cohn Kanade (CK+) face database was used for the experimentation of the system. The classification of the various expressions was achieved by the image category classifier of Matlab.