A fuzzy logic approach for dealing with qualitative quality characteristics of a process

Dealing with qualitative information is quite common in real life problems. So far research focused on developing process adjustment models only for quantitative data. This paper presents a process adjustment approach of a deteriorating process in which quality characteristics are expressed in qualitative form. This approach jointly determines the initial setting of process mean and production run. A fuzzy logic is adopted to implement the process adjustment approach. The features of this approach are lack of mathematical complexity and ability to deal with qualitative data. Detailed implementation of the fuzzy process adjustment model is also given in this paper.     

Heuristic on-line tuning for nonlinear model predictive controllers using fuzzy logic

In this paper a systematic mechanism for on-line tuning of the non-linear model predictive controllers is presented. The proposed method automatically adjusts the prediction horizon P, the diagonal elements of the input weight matrix Λ, and the diagonal elements of the output weight matrix Γ for the sake of good performance. The desired good performance is cast as a time-domain specification. The control horizon (M) is left constant because of the importance of its relative value with respect to P. The concepts from fuzzy logic are used in designing the tuning algorithm. In the mechanism considered here, predefined fuzzy rules represent available tuning guidelines and the performance violation measure in the form of fuzzy sets determine the new tuning parameter values Therefore, the tuning algorithm is formulated as a simple and straightforward mechanism, which makes it more appealing for on-line implementation. The effectiveness of the proposed tuning method is tested through simulated implementation on three non-linear process examples. Two of these examples possess open-loop unstable dynamics. The result of the simulations shows that this method is successful and promising.     

Fuzzy logic models in a category of fuzzy relations

We investigate interpretations of formulas ψ in a first order fuzzy logic in models which are based on objects of a category SetR(Ω) which consists of Ω-sets, i.e. sets with similarity relations with values in a complete MV-algebra Ω and with morphisms defined as special fuzzy relations between Ω-sets. The interpretations are then morphisms in a category SetR(Ω) from some Ω-set to the object . We define homomorphisms between models in a category SetR(Ω) and we prove that if is a (special) homomorphism of models in a category SetR(Ω) then there is a relation between interpretations of a formula ψ in models . Supported by MSM6198898701, grant 201/07/0191 of GAČR and grant 1M0572.     

On fuzzy equality and approximation in fuzzy logic

The paper is a contribution to the theory of fuzzy logic in narrow sense with evaluated syntax (FLn). We show that the concepts of fuzzy equality and the provability degree enable to generalize the concept of fuzzy approximation. In the second part of the paper we return to the Mamdani-Assilian formula, which is formed on the basis of the so called totally bounded fuzzy equality and using which we can approximate any function with the prescribed accuracy.This paper has been supported by Grant A1187901/99 of the GA AV R and the project VS96037 of MMT of the Czech Republic.     

A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic

We describe a new approach for exploiting relevance feedback in content-based image retrieval (CBIR). In our approach to relevance feedback we try to capture more of the users’ relevance judgments by allowing the use of natural language like comments on the retrieved images. Using methods from fuzzy logic and computational intelligence we are able to reflect these comments into new targets for searching the image database. Such enhanced information is utilized to develop a system that can provide more effective and efficient retrieval.     

An efficient posture recognition method using fuzzy logic

Computer-human interaction plays an important role in virtual reality. Glove-based input devices have many desirable features which make direct interactions between the user and the virtual world possible. However, due to the complexity of the human hand, recognising hand functions precisely and efficiently is not an easy task. Existing algorithms are either imprecise or computationally expensive, making them impractical to integrate with VR applications, which are usually very CPU intensive.In the problem of posture and gesture recognition, both the sample patterns stored in the database and the ones to be recognised may be imprecise. This kind of imprecise knowledge can be best dealt with using fuzzy logic. A fast and simple posture recognition method using fuzzy logic is presented in this paper. Our model consists of three components: the posture database, the classifier and the identifier. The classifier roughly classifies the sample postures before they are put into the posture database. The identifier compares an input posture with the records in the identified class and finds the right match efficiently. Fuzzy logic is applied in both the classification and identification processes to cope with imprecise data. The main goal of this method is to recognise hand functions in an accurate and efficient manner. The accuracy, efficiency and the noise tolerance of the model have been examined through a number of experiments.     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

Goguen's contributions to fuzzy logic in retrospect

ABSTRACT

In the very early stage of the development of fuzzy logic, Joseph Goguen published profound work with lasting influence. Fifty years later, we provide an assessment of his contributions to fuzzy logic and thus pay tribute to Goguen, a former long-term member of the editorial board of this journal. We cover both Goguen's technical results, the research directions inspired by him, which have later been pursued, as well as his suggestions, which as yet remained explored to a lesser extent or practically unexplored.     

About axiomatic systems of product fuzzy logic

 This short paper has two goals. The first is to show a new axiomatic system of product fuzzy logic with only one non-BL axiom which has only two variables. The second goal is to prove that there cannot be any axiomatic system of the product fuzzy logic with single non-BL axiom with only one variable.     

Assessing airborne transmission of foot and mouth disease using fuzzy logic

In case of an outbreak of foot and mouth disease, the prediction of airborne spread is an important tool for decision-makers to assess the potential risk of secondary infections. Modelling approaches such as the Gaussian dispersion or Lagrangian particle model have been established but are complex to use and the structure of the models is fixed rather than adjustable to emerging disease situations. The aim of the present study was to evaluate the application of fuzzy logic as a modelling technique based on linguistic variables. Fuzzy logic models are easy to use and to modify. Adaptations to emerging outbreaks seem feasible. Using the Gaussian dispersion model as a reference, livestock-specific fuzzy logic models were developed. In a stepwise modelling process, the input parameters of the Gaussian model were added one-by-one into the fuzzy models. On the basis of weather data and randomly allocated farms, a validation dataset with 10,000 observations was generated and used in a 10-fold cross validation to compare the two modelling approaches. A good agreement between the Gaussian dispersion and the fuzzy logic models concerning the main directions of virus spread were found. The measure of agreement ranged between 87.0% and 99.9%. Falsely classified observations occurred mostly in proximity to the boundary of virus transmission based on the Gaussian dispersion model. In conclusion, fuzzy logic determined the same risk of infection for secondary cases than the Gaussian dispersion model. Limitations to certain livestock were not observed. The inclusion of up to four input variables did not influence the results in a mentionable amount. Including additional input variables into the fuzzy models could improve its application in assessing the risk of airborne foot and mouth disease transmission furthermore.     

基于模糊控制器的自适应广义通用模型控制

广义通用模型控制(GCMC)方法是一般模型控制(GMC)的改进,适用于相对阶大于1的复杂多输入多输出系统,该控制器参数具有明显的物理意义,但鲁棒性不够强。将模糊控制与广义通用模型控制相结合,构成模型参考自适应控制系统,从而加强了系统的鲁棒性,仿真实验证明了该策略的有效性。     

A note on congruence permutability and fuzzy logic

We study congruence permutability of algebras with fuzzy equalities. The notion of degree of congruence permutability naturally arises in this context. We present a Mal'cev-like characterization of congruence permutable varieties of algebras with fuzzy equalities. Our note presents a way to generalize various congruence conditions from the point of view of fuzzy logic.     

Genetic-based fuzzy models: Interest rate forecasting problem

Many phenomena in our lives are difficult to predict. Especially financial markets have eluded successful prediction attempts. Interest rates are quite volatile and nonlinear. We develop the system capable of processing Korean financial data and modeling time-series processes (such as interest rate) with fuzzy logic and genetic algorithms(GAs). In this paper, we bring together two technologies: fuzzy theory and genetic algorithms. The combination of these techniques could be applied to the interest rate forecasting problem in Korean financial market. The fuzzy rules can be concisely represented with one or more FAM (Fuzzy Associative Memory) matrices. We use GAs to adapt the FAM matrix entries so that the interest rate forecasting problem leads to an improved performance. This paper presents the Genetic-Based Fuzzy Model (GBFM).     

Design of local fuzzy models using evolutionary algorithms

The application of local fuzzy models to determine the remaining life of a unit in a fleet of vehicles is described. Instead of developing individual models based on the track history of each unit or developing a global model based on the collective track history of the fleet, local fuzzy models are used based on clusters of peers—similar units with comparable utilization and performance characteristics. A local fuzzy performance model is created for each cluster of peers. This is combined with an evolutionary framework to maintain the models. A process has been defined to generate a collection of competing models, evaluate their performance in light of the currently available data, refine the best models using evolutionary search, and select the best one after a finite number of iterations. This process is repeated periodically to automatically update and improve the overall model. To illustrate this methodology an asset selection problem has been identified: given a fleet of industrial vehicles (diesel electric locomotives), select the best subset for mission-critical utilization. To this end, the remaining life of each unit in the fleet is predicted. The fleet is then sorted using this prediction and the highest ranked units are selected. A series of experiments using data from locomotive operations was conducted and the results from an initial validation exercise are presented. The approach of constructing local predictive models using fuzzy similarity with neighboring points along appropriate dimensions is not specific to any asset type and may be applied to any problem where the premise of similarity along chosen attribute dimensions implies similarity in predicted future behavior.     

Mediative fuzzy logic: a new approach for contradictory knowledge management

In this paper we are proposing a novel fuzzy method that can handle imperfect knowledge in a broader way than Intuitionistic (in the sense of Atanassov) fuzzy logic does (IFL). This fuzzy method can manage non- contradictory, doubtful, and contradictory information provided by experts, providing a mediated solution, so we called it Mediative Fuzzy Logic (MFL). We are comparing results of MFL, with IFL and traditional Fuzzy logic (FL).     

Robust adaptive fuzzy logic power system stabilizer

This paper introduces a robust adaptive fuzzy controller as a power system stabilizer (RFPSS) used to damp inter-area modes of oscillation following disturbances in power systems. In contrast to the IEEE standard multi-band power system stabilizer (MB-PSS), robust adaptive fuzzy-based stabilizers are more efficient because they cope with oscillations at different operating points. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, components that ensure robust and adaptive performance are included in the control law to compensate for modelling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearities. The second system is an adaptive one that compensates for modelling errors. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.     

Filtering multilingual Web content using fuzzy logic and self-organizing maps

Effective multilingual information filtering is required to alleviate users burden of information overload resulting from the increasing flood of multilingual textual content available extensively over the World-Wide Web. This paper proposes a content-based self-organizing approach to multilingual information filtering using fuzzy logic and the self-organizing map. This approach screens and evaluates multilingual documents based on their semantic contents. Correlated multilingual documents are disseminated according to their corresponding themes or topics, thus enabling language-independent content-based information access efficiently and effectively. A Web-based multilingual online news-filtering system is developed to illustrate how the approach works.     

Decision making support system for house purchase by using fuzzy logic

In this paper, we suggest a decision making support system for house purchasers, using fuzzy inference and hierarchic structure of evaluation. Main part of this system consist of macro and micro evaluation. Essential factors are taken into account in macro evaluation, and unessential detailed factors are considered later in micro evaluation. By adopting this structure, many decision makers could get their most suitable result.