I.B. Turksen

TOBB University of Economics and Technology · Department of Industrial Engineering

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper pres...

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, a...

In this paper, a new type-2 fuzzy intelligent agent system (T2F-IAS) for reducing bullwhip effect in a supply chain is proposed. This system uses a special kind of sparse kernel machines, called support vector regression, for forecasting future demands of each echelon in a supply chain. The T2F-IAS includes a data collector agent and a rule generat...

The main task of clustering methods, especially fuzzy methods, is to find whether natural grouping exists in data and to impose identity on them. In some situations, data are stored in several data sites and to discover the global structures, clustering methods have to be aware of dependencies in all data sites. Collaborative fuzzy clustering metho...

In this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi–Sugeno (T–S) systems is presented. Gaussian Mixture Model is used t...

This paper puts forward a location-routing problem with fuzzy demands (LRPFD). A fuzzy chance constrained programming (CCP) model is presented and a simulation-embedded simulated annealing (SA) algorithm is proposed to solve it. Moreover, an initialization heuristic is presented which is based on the well-known fuzzy c-means clustering algorithm. N...

Fuzzy Formal Concept Analysis is a generalization of Formal Concept analysis (FCA) for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web context. However Fuzzy FCA considers type-1 fuzzy rules and does not benefit the opportunities provided by type-2 fuzzy systems. The heterogene...

In this paper we have used an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the risk of stocks. Previous works just predict the return of stocks and make their portfolio based on the predicted return. But for developing a portfolio both risk and return should be predicted. Our model predicts the risk without needing to experts a...

Fuzzy systems approximate highly nonlinear systems by means of fuzzy “if–then” rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the first one is based on inpu...

In this paper a new fuzzy system modeling (FSM) algorithm is introduced as a data analysis and approximate reasoning tool. The performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison two benchmark data sets from the literature, namely the automobi...

This paper proposes a new method for designing Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) considering two issues: first, quality of clustering the output space and secondly, approximating the output of the IT2 FLS based on a new output processing method. Based on these two issues, we present a new cluster validity index capable of being used fo...

In this study, we propose an enhanced fuzzy clustering algorithm related to α-cut interval descriptions of fuzzy numbers and a new cluster validity index, which occurs by α-cut intervals and adding two ad hoc functions in the compactness and separability measures. As an application, we use the enhanced fuzzy clustering algorithm and its proposed va...

In this paper, a new fuzzy functions (FFs) model is presented and its main parameters are optimized with simulated annealing (SA) approach. For this purpose, a new hybrid clustering algorithm for model structure identification is proposed. This model is based on hybridization of extended version of possibilistic c-mean (PCM) clustering with mahalon...

I was introduced to Prof. Dr. Lotfi A. Zadeh in 1970 Summer by Robert Macol, then the President of Operations Research Society of America (ORSA) at a Nato Advanced Study Institute meeting in Istanbul Turkey. In 1971, one of my research students gave me a copy of Zadeh's first paper called Fuzzy Sets. In 1972, Prof. Zadeh came as a member of an accr...

Stock price prediction is an important task for most investors and professional analysts. However, it is a tough problem because of the uncertainties involved in prices. This paper presents a four-layer fuzzy multiagent system (FMAS) architecture to develop a hybrid artificial intelligence model based on the coordination of intelligent agents perfo...

Facility location is a prime decision to be made in many organizations around the globe. The hub location problem is one of the main variants of the facility location problem, with applications in telecommunications, the airline industry, and etc. In this paper, we deal with an incomplete hub-covering network design problem, where the exact locatio...

This paper examines the long-run relationship between the Shanghai index and CRB commodity index. We run our vector error correction model (VECM) for two sub-samples as pre-crisis period and post-crisis period. In pre-crisis period, there is strong bidirectional causality link between the Shanghai and CRB. In post-crisis period, there is no causali...

After more than three decades since the introduction of linguistic variables and their application to approximate reasoning by Zadeh [1], the ability of fuzzy logic systems (FLSs) for modeling real world applications is not a secret to anyone. Currently there are two basic approaches to determine fuzzy model of a system in the literature which are,...

The aim of this paper is developing the optimization model for global market analysis. The type-2 fuzzy model is developed based on the variables which indicate the export trade trend during the specified period. The proposed model is implemented for forecasting export value of international market segment of Parisian carpet. This model can be used...

In this paper, a type-2 fuzzy TSK expert system is developed for optimizing the global market prediction. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the variables which indicate the export trade trend during the specif...

This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure identification phase of Takagi–Sugeno (T–S) systems. We present uncertainties with fuzzifier parameter “m”. In order to identify the parameters of interval type-2 fuzzy sets, two fuzzifiers “m1” and “m2” are used. Then, by utilizing these two fuzzifiers in a...

Uncertainty is a central part of many data analysis methodologies. Although quantifying the uncertainty has long been discussed, the research on it is still in progress. The level of fuzziness in fuzzy system modeling is a source of uncertainty which can be classified as a parameter uncertainty. Upper and lower values of the level of fuzziness for...

A systems modeling is proposed with a unification of fuzzy methodologies. Three knowledge representation schemas are presented together with the corresponding approximate reasoning methods. Unsupervised learning of fuzzy sets and rules is reviewed with recent developments in fuzzy cluster analysis techniques. The resultant fuzzy sets are determined...

In a historical context, we first review the development of fuzzy system models from “Fuzzy Rule Bases” proposed by Zadeh (1975) [1], with versions of Sugeno–Yasukawa (1993) [2] and Tagaki–Sugeno (1985) [3]. Secondly, we review the development of the “Fuzzy C-Regression Model” (FCRM), proposed by Hathaway and Bezdek (1993) [4], as well as “Combined...

Our native intelligence captures and encodes our knowledge into our biological neural networks, and communicates them to the external world via linguistic expressions of a natural language. These linguistic expressions are naturally constrained by syntax and semantics of a given natural language and its cultural base of abstractions. Next, an accep...

Data, as being the vital input of system modelling, contain dissimilar level of imprecision that necessitates different modelling approaches for proper analysis of the systems. Numbers, words and perceptions are the forms of data that has varying levels of imprecision. Existing approaches in the literature indicate that, computation of different da...

This paper deals with the problem of locating new post offices in a megacity. To do so, a combination of geographical information system (GIS) and fuzzy goal programming (FGP) is used. In order to locate new offices, first six types of service facilities with high levels of interactions with post offices are defined. Then, aspiration level of proxi...

There has been enormous interest about Covering Location Problem (CLP) among both academicians and practitioners around the world. Applications of CLP range from locating fire stations to telecommunications. This paper deals with a special case of CLP where travel times are fuzzy variables. In addition, it has been assumed that the variable cost of...

We analyze the impact of imprecise parameters on performance of an uncertainty-modeling tool presented in this paper. In particular, we present a reliable and efficient uncertainty-modeling tool, which enables dynamic capturing of interval-valued clusters representations sets and functions using well-known pattern recognition and machine learning a...

A Hub Location Problem (HLP) deals with finding the locations of hub facilities and assignment of demand nodes to established facilities. Due to special characteristics of HLP, the overall performance of the network highly depends on proper performance of hubs. Therefore, the design of reliable networks is a critical issue to be considered. In this...

Organizations have to be competitive in their market throughout the globalization process. Thus, they need to achieve customer demands in strategic ways focusing on customer satisfaction. At this point, decision makers encounter with uncertainties based on reducing the deviation in the production/service processes. The uncertainty problem of suppli...

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally...

Location Set Covering Problem (LSCP) is a traditional problem in the location literature. LSCP is used in locating fire stations, computer networks, and many other service facilities. This paper proposes a covering problem with variable radii. In this problem, the cost to establish a facility is a monotonically increasing function of distance to th...

In this paper a new intelligent multi-agent system is proposed for finding the best ordering policy. The best ordering policy is the policy which minimizes the total cost of the supply chain that is the sum of all echelons' costs over all periods. The best ordering policy is obtained by a new window-base genetic algorithm. One limitation of the pre...

This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to...

In this study, we propose fuzzy modeling algorithm to improve Takagi-Sugeno fuzzy model. This algorithm initially finds desirable number of rules at once, in advance, and then identifies the premise and consequent parameters separately by fixing number ...

This study aims to analyze the effect of country size, represented by relative Gross National Products (GNP), on the association between domestic investment and saving, using data from a panel of 21 OECD countries. The countries are clustered into four groups with respect to their relative country sizes with an application of Fuzzy c-Means clusteri...

This paper presents a multi-agent system (MAS) for reduction of the bullwhip effect in fuzzy supply chains. First, it is shown that, even using an optimal ordering policy, without data sharing the bullwhip effect still exists in the supply chain. Then a multi-agent system is proposed to manage the bullwhip effect. The multi-agent system has four di...

In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order...

The objective of this paper is to show the strength of a modified version of particle swarm optimization (PSO) in definition of suitable partitions of fuzzy time series forecasting and increasing its accuracy. Although a lot of contributions have been made to increase the quality of forecasts using fuzzy time series , there are only a few papers co...

In building an approximate fuzzy classifier system, significant effort is laid on estimation and fine-tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-c...

Fuzzy inference systems based on fuzzy rule bases (FRBs) have been successfully used to model real problems. Some of the limitations exhibited by these traditional fuzzy inference systems are that there is an abundance of fuzzy operations and operators that an expert should identify. In this paper we present an alternate learning and reasoning sche...

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is teste...

Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provid...

Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-re...

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. They rely on graphs that jointly represent each data point. The problem of how to best formulate the graph representation remains an open research topic. In this paper, we introduce a type-2 fuzzy arithm...

This chapter introduces a new uncertainty modeling architecture for the new improved fuzzy functions systems. The theory is based on a new interval type-2 fuzzy system. The uncertainties are captured by automatic identification of the structure of fuzzy functions, and upper and lower boundaries of key parameters that define fuzzy sets. A new type r...

The new fuzzy system modeling approach based on fuzzy functions implements fuzzy clustering algorithm during structure identification of the given system. This chapter introduces foundations of fuzzy clustering algorithms and compares different types of well-known fuzzy clustering approaches. Then, a new improved fuzzy clustering approach is presen...

This chapter reviews basic principles of the fuzzy sets and fuzzy logic as well as the inference methodology of the approximate reasoning and the extension principle theories that are fundamental parts of structure identification with traditional fuzzy rule base systems. Also presented is the “Fuzzy Functions” as defined in the literature and as pr...

This chapter presents the results of experiments applied to benchmark and real life datasets to evaluate the performance of proposed algorithms. The results are compared to other soft computing methods of system modeling. In this chapter, performance of the proposed Fuzzy Functions approaches is analyzed against performances of other well-known sof...

Through the emergence of information and communication technologies and customer-oriented approaches in business and industry, for achieving competitive advantages and in order to remain at the top in every business, more and responsive supply chain systems are required. The next generation of supply chain systems must be agile, adaptive, cooperati...

This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if ... then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually...

The fuzzy C-regression Method (FCRM) based on fuzzy C-means (FCM) clustering algorithm was proposed by Hathaway and Bezdek to solve the switching regression problems, and it was applied to fuzzy models by many to build more powerful fuzzy inference systems. The FCRM methods require initialization parameters which are in need for proper identificati...

Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to mo...

A new type-2 fuzzy classifier function system is proposed for uncertainty modeling using genetic algorithms - GT2FCF. Proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy classifier function systems induced by learning parameters, as well as fuzzy classifier functions. Hidden structures are captured with...

Suppliers play a pivotal role in success of any organization. Supplier Selection is a complicated problem due to the vagueness of data and also its multi-criteria nature and the real world still observes a noticeable gap between its theory and practice. The aim of this paper is to present a fuzzy decision-making approach to address this problem in...

“Fuzzy Functions” are proposed to be determined by the least squares estimation (LSE) technique for the development of fuzzy system models. These functions, “Fuzzy Functions with LSE” are proposed as alternate representation and reasoning schemas to the fuzzy rule base approaches. These “Fuzzy Functions” can be more easily obtained and implemented...

In this paper, we attempt to analyze currency crises within the decision theory framework. In this regard, we employ fuzzy system modeling with fuzzy C-means (FCM) clustering to develop perception based decision matrix. We try to build a prescriptive model in order to determine the best approximate reasoning schemas. We use the underlying behavior...

This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzi...

In crisp run control rules, usually it is stated that a process moves very sharply from in-control condition to out-of-control act. This causes an increase in both false-alarm rate and control chart sensitivity. Moreover, the classical run control rules are not implemented on an intelligent sampling strategy that changes control charts’ parameters...

The main purpose of this paper is to develop fuzzy polynomial neural networks (FPNN) to predict the compressive strength of concrete. Two different architectures of FPNN are addressed (Type1 and Type2) and their training methods are discussed. In this research, the proposed FPNN is a combination of fuzzy neural networks (FNNs) and polynomial neural...

We introduce two new criterions for validation of results obtained from recent novel-clustering algorithm, improved fuzzy clustering (IFC) to be used to find patterns in regression and classification type datasets, separately. IFC algorithm calculates membership values that are used as additional predictors to form fuzzy decision functions for each...

The level of fuzziness is a parameter in fuzzy system modeling which is a source of uncertainty. In order to explore the effect of this uncertainty, one needs to investigate and identify effective upper and lower boundaries of the level of fuzziness. For this purpose, Fuzzy c-means (FCM) clustering methodology is investigated to determine the effec...

A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods. It is a new alternate version of the earlier FSM with fuzzy functions approaches. SVM is applied to determine th...

This paper presents a new evolutionary fuzzy system modeling strategy alternative to fuzzy rule bases, and does not entail if...then rule base structure. The new approach, which is based on improved fuzzy functions with genetic algorithms, is proposed to reduce complexity of earlier fuzzy system models and improve modeling accuracy. Structure ident...

A new cluster validity index is introduced to validate the results obtained by the recent Improved Fuzzy Clustering (IFC), which combines two different methods, i.e., fuzzy c-means clustering and fuzzy c-regression, in a novel way. Proposed validity measure determines the optimum number of clusters of the IFC based on a ratio of the compactness to...

In this paper, a type-2 Fuzzy Rule Based Expert System is developed for analysing the stock markets. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. The fuzzy ru...

This paper presents a new fuzzy classifier design, which constructs one classifier for each fuzzy partition of a given system. The new approach, namely Fuzzy Classifier Functions (FCF), is an adaptation of our generic design on Fuzzy Functions to classification problems. This approach couples any fuzzy clustering algorithm with any classification m...

This research deals with the problems of the necessarily critical paths in the networks with imprecise durations and time lags, represented by intervals or fuzzy numbers. So far, the related problems have been solved when the activity durations are imprecise, by several authors. However, they do not consider the impressions in time lags in their mo...

Fuzzy System Modeling (FSM) approaches are renowned to identify non-linear system behaviors under uncertainty by implementing higher types of fuzzy sets, To capture some of the uncertainties, in this paper a new alternative learning and reasoning strategy is presented, which does not entail if…then rule-base structure. Proposed modeling approach id...

Fuzzy System Models (FSM), as one of the constituents of soft computing methods, are used for mining implicit or unknown knowledge by approximating systems using fuzzy set theory. The undeniable merit of FSM is its inherent ability of dealing with uncertain, imprecise, and incomplete data and still being able to make powerful inferences. This paper...

In this paper, first, the main problems of some cluster validity indices when they have been applied to Gustafson and Kessel (GK) clustering approach are review. It is shown that most of these cluster validity indices have serious shortcomings to validate Gustafson Kessel algorithm. Then, a new cluster validity index based on a similarity measure o...

Fuzzy system modeling (FSM) is one of the most prominent tools that can be used to identify the behavior of highly nonlinear systems with uncertainty. Conventional FSM techniques utilize type 1 fuzzy sets in order to capture the uncertainty in the system. However, since type 1 fuzzy sets express the belongingness of a crisp value x' of a base varia...

This paper presents a new type-2 fuzzy logic system model for desulphurization process of a real steel industry in Canada. The type-2 fuzzy logic system permits us to model rule uncertainties where every membership value of an element has a second order membership value of its own. In this paper, we propose an indirect method to create second order...

Feature selection is one of the most important issues in the research fields such as system modelling, data mining and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains....

In this paper, we propose an indirect method to fuzzy modeling which implements a clustering algorithm to build a linguistic fuzzy controller. Based on output data clustering and projection onto the input spaces, the number of clusters is determined and rules are generated automatically. A new methodology based on output sensitivity is developed fo...

As a foundation for Computing With Words, meta-linguistic axioms are proposed in analogy to the axioms of classical theory. Consequences of these meta-linguistic expressions are explored in the light of Interval-valued Type 2 Fuzzy Sets. This once again demonstrates that fuzzy set theories and hence CWW have a richer and more expressive power that...

An Ontological and Epistemological foundation of Fuzzy Set and Logic Theory is reviewed in comparison to Classical Set and Logic Theory. It is shown that basic equivalences of classical theory breakdown but are re-established as weak equivalences as a containment relation in fuzzy theory. It is also stressed that the law of conservation of informat...

In this paper, a fuzzy model is proposed for supplier selection and to identify proper Supplier Development Programs (SDPs). A fuzzy rule base is used to calculate the utility value of a firm’s managers to perform a special SDP. Implementation of the model is demonstrated by a numerical example.