Fuzzy Rule Interpolation Research Papers (original) (raw)

The ability to predict mechanical properties of thermoplastic composites in order to satisfy the performance requirements is of great importance in course of the design. In this paper, a general meth od group for data driven fuzzy... more

The ability to predict mechanical properties of thermoplastic composites in order to satisfy the performance requirements is of great importance in course of the design. In this paper, a general meth od group for data driven fuzzy modeling and its applic ation is presented. Two low complexity fuzzy models were generated for the prediction of Charpy impact stren gth and yield strength as a function of the percent amo unt of the components. The models take as input parameters he percentage of the nanotube and ABS

This paper reports on the use of a fuzzy rule interpolation technique for the modelling of hydrocyclones. Hydrocyclones are important equipment used for particle separation in mineral processing industry . Fuzzy rule based systems are... more

This paper reports on the use of a fuzzy rule interpolation technique for the modelling of hydrocyclones. Hydrocyclones are important equipment used for particle separation in mineral processing industry . Fuzzy rule based systems are useful in this application domains where direct control of the hydrocyclone parameters is desired. It has been reported that a rule extracting technique has been

Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to address sparse fuzzy rule bases and reduce complexity. The first FRI method was the Koczy and Hirota (KH) proposed "Linear Interpolation". Besides, several... more

Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to address sparse fuzzy rule bases and reduce complexity. The first FRI method was the Koczy and Hirota (KH) proposed "Linear Interpolation". Besides, several conditions and criteria have been suggested for unifying the common requirements FRI methods have to satisfy. One of the most conditions is restricted the fuzzy set of the conclusion must preserve a Piece-Wise Linearity (PWL) if all antecedents and consequents of the fuzzy rules are preserving on PWL sets at α-cut levels. The KH FRI is one of FRI methods which cannot satisfy this condition. Therefore, the goal of this paper is to investigate equations and notations related to PWL property, which is aimed to highlight the problematic properties of the KH FRI method to prove its efficiency with PWL condition. In addition, this paper is focusing on constructing benchmark examples to be a baseline for testing other FRI methods against situations that a...

The ability to predict mechanical properties of thermoplastic composites in order to satisfy the performance requirements is of great importance in course of the design. In this paper, a general meth od group for data driven fuzzy... more

The ability to predict mechanical properties of thermoplastic composites in order to satisfy the performance requirements is of great importance in course of the design. In this paper, a general meth od group for data driven fuzzy modeling and its applic ation is presented. Two low complexity fuzzy models were generated for the prediction of Charpy impact stren gth and yield strength as a function of the percent amo unt of the components. The models take as input parameters he percentage of the nanotube and ABS

In the last thirty years fuzzy logic became very popular. One can find solutions based on it in several fields from industrial systems to house appliances. Recently a new category of fuzzy systems gained more attention, the so called... more

In the last thirty years fuzzy logic became very popular. One can find solutions based on it in several fields from industrial systems to house appliances. Recently a new category of fuzzy systems gained more attention, the so called fuzzy rule interpolation (FRI) based systems. Owing to the low complexity of their rule bases, i.e. they can infer as well when only the relevant rules are known, they can be applied successfully even in cases when a traditional fuzzy system could not give an interpretable result in lack of the full coverage of the rule base. In this paper, after doing a survey on FRI methods we present several successful practical applications organized in three main areas, namely fuzzy control, function approximation and expert systems.

The goal of this paper is twofold. Once to highlight some basic problematic properties of the KH Fuzzy Rule Interpolation through examples, secondly to set up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy... more

The goal of this paper is twofold. Once to highlight some basic problematic properties of the KH Fuzzy Rule Interpolation through examples, secondly to set up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule Interpolation methods were originally proposed to handle the situation of missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity. Fuzzy Rule Interpolation is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. The first FRI method was the Koczy and Hirota proposed "Linear Interpolation", which was later renamed to "KH Fuzzy Interpolation" by the followers. There are several conditions and criteria have been suggested for unifying the common requirements an FRI methods have to sat...

In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not... more

In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not always available. Fuzzy control systems based on the Fuzzy Rule Interpolation (FRI) concept play a major role in different platforms, in case if only a sparse fuzzy rule-base is available. This cases the fuzzy model contains only the most relevant rules, without covering all the antecedent universes. The first FRI toolbox being able to handle different FRI methods was developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of this paper is to introduce some details of the adaptation of the FRI toolbox to support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming language, providing a large functionally compatible subset of the MATLAB FRI toolbox as ...

Fuzzy systems built on sparse rule bases apply special inference techniques. A large family of them can be described by the concept of the general methodology of the fuzzy rule interpolation (GM) [1]. Accordingly to this the conclusion is... more

Fuzzy systems built on sparse rule bases apply special inference techniques. A large family of them can be described by the concept of the general methodology of the fuzzy rule interpolation (GM) [1]. Accordingly to this the conclusion is produced in two steps. First a new rule is interpolated corresponding to the position of the reference point of the observation in each antecedent dimension. Secondly the conclusion is determined by firing this rule. This paper proposes a novel set approximation method (FEAT-p) applicable in the first step of the GM for the determination of the antecedent and consequent sets of the new rule. The suggested technique introduces the concept of the polar cut and calculates the points of the shape of the sets taking into consideration all sets belonging to the actual partition. The method can handle subnormal sets, too.

Approximate fuzzy reasoning methods serves the task of inference in case of fuzzy systems built on sparse rule bases. This paper is a part of a longer survey that aims to provide a qualitative view through the various ideas and... more

Approximate fuzzy reasoning methods serves the task of inference in case of fuzzy systems built on sparse rule bases. This paper is a part of a longer survey that aims to provide a qualitative view through the various ideas and characteristics of interpolation based fuzzy reasoning methods. It also aims to define a general condition set for fuzzy rule interpolation methods brought together from an application-oriented point of view. The methods being presented also can be applied in the first level of systems built on hierarchical fuzzy rule bases.

Fuzzy systems built on sparse rule bases apply special inference techniques. A large family of them can be described by the concept of the general methodology of the fuzzy rule interpolation (GM) [1]. Accordingly to this the conclusion is... more

Fuzzy systems built on sparse rule bases apply special inference techniques. A large family of them can be described by the concept of the general methodology of the fuzzy rule interpolation (GM) [1]. Accordingly to this the conclusion is produced in two steps. First a new rule is interpolated corresponding to the position of the reference point of the observation in each antecedent dimension. Secondly the conclusion is determined by firing this rule. This paper proposes a novel set approximation method (FEAT-p) applicable in the first step of the GM for the determination of the antecedent and consequent sets of the new rule. The suggested technique introduces the concept of the polar cut and calculates the points of the shape of the sets taking into consideration all sets belonging to the actual partition. The method can handle subnormal sets, too.

Application of fuzzy automata and interpolative fuzzy reasoning in Kansei Technology gives a simple way for adding user adaptivity to emotion-based selection systems (like interactive furniture selection based on human feelings in our... more

Application of fuzzy automata and interpolative fuzzy reasoning in Kansei Technology gives a simple way for adding user adaptivity to emotion-based selection systems (like interactive furniture selection based on human feelings in our case). One way of handling user adaptivity in emotion-based systems is a kind of combination of existing (already collected) human opinions. To add user adaptivity, this combination must be done in the function of the approximated similarities of the actual user to the existing human opinions (user models). This kind of systems has two main tasks, namely approximating the similarities of the actual user opinions to the existing user models, and the next is to combine these models in the function of the corresponding approximated similarities to get the approximated actual user model. Here we suggest to apply fuzzy automata and interpolative fuzzy reasoning for a simple way of solving these tasks.

Fuzzy logic systems based on If-Then rules are widely used for modelling of the systems characterizing imprecise and uncertain information. These systems are basically based on type-1 fuzzy sets and allow handling the uncertain and... more

Fuzzy logic systems based on If-Then rules are widely used for modelling of the systems characterizing imprecise and uncertain information. These systems are basically based on type-1 fuzzy sets and allow handling the uncertain and imprecise information to some degree in the developed models. Zadeh extended the concept of fuzzy sets and proposedZ-number characterized by two components, constraint and reliability parameters, which are an ordered pair of fuzzy numbers. Here, the first component is used to represent uncertain information, and the second component is used to evaluate the reliability or the confidence in truth.Z-number is an effective approach to solving uncertain problems. In this paper,Z-number-based fuzzy system is proposed for estimation of food security risk level. To construct fuzzy If-Then rules, the basic parameters cereal yield, cereal production, and economic growth affecting food security are selected, and the relationship between these input parameters and ri...

Fuzzy systems based on sparse rule bases produce the conclusion through approximation. This paper is the first part of a longer survey that aims to provide a qualitative view through the presentation of the basic ideas and characteristics... more

Fuzzy systems based on sparse rule bases produce the conclusion through approximation. This paper is the first part of a longer survey that aims to provide a qualitative view through the presentation of the basic ideas and characteristics of some methods and defining a general condition set brought together from an application- oriented point of view.

In this paper we suggest that social robots should not mirror exactly human social behavior (facial expressions, language, etc) but need to be able to produce believable social behaviors that provide a minimal set of actions by which... more

In this paper we suggest that social robots should not mirror exactly human social behavior (facial expressions, language, etc) but need to be able to produce believable social behaviors that provide a minimal set of actions by which human-companion cooperation can be achieved. For implementing such an ethologically inspired social robot behavior, a platform based on fuzzy automaton (fuzzy state-machine) is suggested.

The concept of 'Future Internet', 'Internet of Things' and '3D Internet' opens a novel way for modeling ethological tests by rebuilding models of human-animal interaction in an augmented environment as an... more

The concept of 'Future Internet', 'Internet of Things' and '3D Internet' opens a novel way for modeling ethological tests by rebuilding models of human-animal interaction in an augmented environment as an interactive mixture of virtual actors and real human observers. On the one hand these experiments can serve as a proof of concept, as a kind of experimental validation of formal ethological models, but on the other hand they can also serve as examples for the ways a human can communicate with things (i.e., with everyday objects) in a virtual environment (e.g. on the Internet). These kinds of experiments can also support Cognitive Infocommunication related research, the field that investigates how a human can co-evolve with artificially cognitive systems through infocommunications devices. The goal of the paper is to introduce an example for such an ethological test system, a possible way for embedding a prototype ethological model described as a fuzzy automa...

The main idea of behaviour-based control structures is to handle partially known complex situations by a set of known behaviours. By discrete switching to the behaviour seems to be the most appropriate one, or by fusing the behaviours... more

The main idea of behaviour-based control structures is to handle partially known complex situations by a set of known behaviours. By discrete switching to the behaviour seems to be the most appropriate one, or by fusing the behaviours appeared to be the most appropriate ones. These structures has two main tasks to solve. The decision about the level of suitability of the known behaviours in handling the actual situation, and the way of their fusing to form the actual behaviour. In this paper, for these tasks, a flexible structure, the fuzzy automaton based system state approximation and the interpolative fuzzy reasoning based fusion is suggested. For demonstrating the applicability of the suggested structure, a path tracking and collision avoidance navigation control of a simulated automated guided vehicle (AGV) is also introduced briefly in this paper.

Some difficulties emerging during the construction of fuzzy rule bases are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of... more

Some difficulties emerging during the construction of fuzzy rule bases are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of fetching fuzzy rules directly from expert knowledge, the way of building a complete rule base is not always straightforward. One simple solution for overcoming the necessity of the complete rule base is the application of interpolation-based fuzzy reasoning methods, since interpolation- based fuzzy reasoning methods can serve usable (interpolated) conclusion even if none of the existing rules is hit by the observation. These methods can save the expert from dealing with derivable rules and help to concentrate on cardinal actions only. For demonstrating the benefits of the interpolation-based fuzzy reasoning methods in construction of fuzzy rule bases a simple example will be introduced briefly in this paper too.