Publications

Fuzzy Clustering in Software Reusability

This paper is concerned with the organization and retrieval of reusable software components with the aid of unsupervised learning. The methods considered of unsupervised learning include FUZZY ISODATA and Kohonen self-organizing maps. The key issues addressed in the study include information retrieval in the presence of incomplete information, and domain specific enhancements of unsupervised learning, including those of partial supervision. The primary intention is to reveal how the learning mechanism can accommodate individual preferences (profile) of the users viewed as a significant component of organization and retrieval algorithms. Numerical examples use a set of MS-DOS system commands and a collection of reusable C++ classes. © 1997 by John Wiley & Sons, Ltd.

Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks

We propose a new category of neurofuzzy networks—self-organizing neural networks (SONN) with fuzzy polynomial neurons (FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are discussed. Each of them comes with two topologies such as a generic and advanced type. Especially in the advanced type, the number of nodes in each layer of the SONN architecture can be modified with new nodes added, if necessary. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. The architecture of the SONN is not fixed in advance as it usually takes place in the case of conventional neural networks, but becomes organized dynamically through a growth process. Simulation involves a series of synthetic as well as real-world data used across various neurofuzzy systems. A comparative analysis shows that the proposed SONN are models exhibiting higher accuracy than some other fuzzy models.

Information Granule Based Uncertainty Measure of Fuzzy Evidential Distribution

Quantifying the uncertainty of information distributions containing randomness, imprecision, and fuzziness is the premise of processing them. A useful information representation in the field of intelligent computing are information granules, which optimize data from the perspective of specificity and coverage. We introduce information granularity into evidential information and model the basic probability assignment (BPA) as a weighted information granules model. Based on the proposed model, a new uncertainty measure of BPA is derived from the quality evaluation of granules. In addition, the proposed measure is extended to fuzzy evidential information distributions. When the Fuzzy BPA (FBPA) degenerates into the Probability Mass Function (ProbMF) and Possibility Mass Function (PossMF), the proposed method degenerates to Gini entropy and Yager's specificity measure, respectively. We use a refined belief structure to interpret the meaning of FBPA in the transfer belief model, and verify the validity of the proposed method by analyzing its properties and presenting numerical examples. The concept of information granule is used for the first time to model focal set and beliefs. Compared with Shannon entropy based information measures, the proposed method provides a novel perspective on the relationship between randomness, imprecision, and fuzziness in FBPA.

Thunderstorm Detection and Prewarning via Multisource Fusion of Precipitation and Atmospheric Electric Field Features

Traditional detection and prewarning methods based on a single atmospheric electric field (AEF) data source often fail to accurately assess thunderstorm weather conditions. This article proposes a thunderstorm detection method that employs weighted multisource fusion of precipitation and AEF features, achieving a high-precision evaluation through multimodal feature fusion and machine learning model optimization. Based on the detection range of AEF apparatus, spatially matched radar chart precipitation data are extracted to construct physically meaningful precipitation statistic features and time-varying AEF features. Using feature importance analysis, the physical contribution of each feature is determined, and these features are weighted and fused accordingly to generate enhanced features. Furthermore, we establish an improved gradient boosting decision tree model that enhances classification performance in the weighted feature space by adaptively adjusting the learning rate. Experimental results demonstrate that this method achieves a highly competitive F1-score of 0.92 in thunderstorm recognition tasks, representing a 35.3% improvement over the traditional AEF threshold method. The proposed weighted fusion framework provides a novel solution for thunderstorm detection and prewarning through the synergistic use of ground-based AEF and radar-based precipitation data.

An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

GRNN Model With Feedback Mechanism Incorporating <i>k</i>-Nearest Neighbor and Modified Gray Wolf Optimization Algorithm in Intelligent Transportation

Uninorm Based Fuzzy Network for Tree Data Structures

Negative Hesitation Soft Fuzzy Sets and its Application on Decision Making Problems

In this paper, we propose a concept on fuzzy soft set with negative hesitation degree. Considering the unclear information on fuzzy sets, it is represented as the overlap with the intersection of known sets over the universe of discourse. The hesitation part is clarified with detail by analyzing the overlap area belonging to the intersection of known sets or not involving in any known sets. It also resolves the limitations in intuitionistic fuzzy sets and Pythagorean fuzzy sets. With the combination of membership degree and non-membership degree, (negative)hesitation is defined in their domain. From the definition, it is more flexible for fuzzy sets characterizing when the negative hesitation degree used to describe information.

A new approach to the fractional Abel k−integral equations and linear fractional differential equations in a fuzzy environment

Multigranulation Supertrust Model for Attribute Reduction

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this article, we present a novel multigranulation supertrust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation supertrust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme was adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular coevolution is employed to ensure a wide range of balancing of exploration and exploitation, and this strategy can classify super elitists' preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms

Fuzzy Clustering

GRANULAR FUZZY SYSTEMS: A NEW DIRECTION IN SOFT COMPUTING AND HUMAN CENTRIC DECISION-MAKING

Ranking multiple aspect alternatives—Fuzzy relational equations approach

Solving fuzzy relational equations through logical filtering

Granular representation and granular computing with fuzzy sets

Information granule-based classifier: A development of granular imputation of missing data

Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution