Publications

In this paper, the fundamental idea of linguistic models introduced by Pedrycz and Vasilakos (1999) is followed and their comprehensive design framework is developed. The paradigm of linguistic modeling is concerned with constructing models that: 1) are user centric and 2) inherently dwell upon collections of highly interpretable and user-oriented entities such as information granules. The objective of this paper is to investigate and compare alternative design options, present an organization of the overall optimization process, and come up with a specification of several evaluation mechanisms of the performance of the models. The underlying design tool guiding the development of linguistic models revolves around the augmented version of fuzzy clustering known as a context-based or conditional fuzzy C-means (C-FCM). The design process comprises several main phases such as: 1) defining and further refining context fuzzy sets; 2) completing conditional fuzzy clustering; and 3) optimizing parameters (connections) linking information granules in the input and output spaces. An iterative process of forming information granules in the input and output spaces is discussed. Their membership functions are adjusted by the gradient-based learning guided by the minimization of some performance index. The paper comes with a comprehensive suite of experiments that lead to some design guidelines of the models. Furthermore, the performance of linguistic models is contrasted with that of other fuzzy models, especially radial basis function neural networks (RBFNNs) and related constructs that are based on concepts of fuzzy clustering.

Intuitionistic fuzzy (IF) theory has become main approach to representing imprecision and vagueness. The IF divergence measure (IFDivM) based on Jensen–Shannon divergence is perhaps the most widely used measure to compare the similarity of multiple intuitionistic fuzzy sets (IFSs). In the present paper, this IFDivM is examined and applied to multiple examples. It is found that some extant IFDivMs hardly satisfy the axiomatic definition, and in a few cases even unable to show divergence of trivial IFSs. To address these inconsistencies, a new IFDivM based on Jensen–Shannon divergence is proposed, free from these problems. The effectiveness of the proposed IFDivM is tested on several critical cases, and precise analysis of its properties is performed. It is proved that the proposed IFDivM satisfies the axiomatic definition of IFDivMs. To illustrate the practical significance of the IFDivM, a novel intuitionistic fuzzy (IF) TODIM method, based on the proposed IFDivM, is developed, termed as GIF-TODIM method. Unlike the existing IF-TODIM methods, GIF-TODIM does not suffer from the revere ordering inconsistencies. The proposed GIF-TODIM method and the proposed IFDivM are applied to a real-world case study on supplier selection. A detailed comparative analysis is performed taking the TOPSIS method and other IFDivMs as baselines. The role of attitude on the final choice is analyzed in great detail. It is found that the proposed GIF-TODIM method is indeed useful, effective, and superior to the counterpart methods, when it comes to real-world situations. Concomitantly, in the present work, it is also revealed that the TOPSIS method based on the 2-D Hamming distance is a special form of the proposed GIF-TODIM method, when decision-makers have the same attitude towards losses and gains. Thus, an interesting relationship between TOPSIS and TODIM is identified under the intuitionistic fuzzy environment, which is bound to propel significant research in the area of decision making under uncertain conditions. As a whole, the article offers comprehensive analyses of IFDivMs and the TODIM method under the intuitionistic fuzzy environment.

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

In this paper, we introduce new architectures of genetically oriented fuzzy relation neural networks (FrNNs) and offer a comprehensive design methodology that supports their development. The proposed FrNNs are based on ldquoif-thenrdquo-rule-based networks, with the extended structure of the premise and the consequence parts of the individual rules. We consider two types of the FrNN topologies, which are called FrNN-I and FrNN-II here, depending upon the usage of inputs in the premise and the consequence of fuzzy rules. Three different forms of regression polynomials (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to develop optimal FrNNs, the structure and the parameters are optimized using genetic algorithms (GAs). The proposed methodology is compared when the two development strategies, with separate and simultaneous optimization schemes that involve structure and parameters, are carried out. Given the large search space associated with these FrNN models, we enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FrNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FrNNs, we exploit a suite of several representative numerical examples. A comparative analysis shows that the FrNNs exhibit higher accuracy and predictive capabilities as well as better modeling stability, when compared with some other models that exist in the literature.

Building transparent and highly interpretable models of the construction performance is generally of significant importance to construction managers. However, previous research focuses more on the approximation accuracy of construction performance models. Few studies have been done on the transparency of models, i.e., offering some understandable cause-effect relationships between the construction performance indicator and its influence factors. In this paper, a transparent construction performance model is proposed. First, a neural network, named General Regression Neural Network (GRNN) is selected as the basic modeling technique. Its new genetic algorithm based learning algorithm is introduced. The GRNN not only presents a high approximation rate, but also offers importance indices about the influence of inputs on the output. Secondly, a fuzzy clustering algorithm is introduced to granulate the inputs into their linguistic terms. The model built with the use of granulated data provides clearer influence factors and the indicator of resulting construction performance. The proposed method is tested on the data collected from construction sites. The results demonstrate the feasibility and efficiency of the proposed model

Information granules are fundamental, abstract, and easy-to-operate constructs supporting the human-centered handling way in granular computing (GrC). One of the basic properties of information granules comes with its hierarchy. Information granularity is the quantification expression of hierarchy of an information granule. Forming information granules with multigranularity to hierarchically describe the nature of data is an important task in GrC. In this article, a cone-shaped fuzzy set-based granular description method with multigranularity is proposed for multidimensional data. Its fundamental idea is to realize the synergy of quantification of information granularity and a hierarchical description of data by means of α-cuts of cone-shaped fuzzy sets. The proposed method first partition the entire data set into a series of data chunks. Then, some mutually nonoverlapping cone-shaped fuzzy sets are constructed by optimally determining their cores and support radii in terms of the coverage of α-cuts of these fuzzy sets to the data located in individual chunks and the corresponding specificity. Finally, by implementing α-cuts processing for these constructed cone-shaped fuzzy sets, a collection of families of hyper-spherical information granules used to hierarchically describe the structural characteristics of the data set are completely emerged. Besides, the quality of the resulting families of hyper-spherical information granules is evaluated along the granular perspective and the application perspective, respectively. A series of experimental studies concerning several synthetic and publicly available data sets are covered. The experimental results demonstrate the superiority of the proposed granular description method of data with multigranularity.