Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to
describe the uncertain information approximately in rough set theory. Certain and uncertain …

摘要在大数据时代, 如何充分挖掘出蕴藏于数据资源中的价值正在成为各国IT 业界,
学术界和政府共同关注的焦点. 使用云计算平台分布式地存储和分析大数据已经成为共识并且 …

In this study, we propose a cluster-oriented development of fuzzy models. An overall design
process is focused on an efficient usage of fuzzy clustering, Fuzzy C-Means (FCM), in …

In this paper, an interval type-2 radial basis function neural network (IT2-RBF-NN) is
proposed as a new modeling framework. We take advantage of the functional equivalence …

Granule description is a fundamental problem in granular computing. Although the spirit of
granular computing has been widely adopted in scientific researches, how to classify and …

This paper introduces granular computing (GrC) into formal concept analysis (FCA). It
provides a unified model for concept lattice building and rule extraction on a fuzzy …

In this paper, we introduce a concept of a granular neural network and develop its
comprehensive design process. The proposed granular network is formed on the basis of a …

Granular computing is a computational paradigm that mimics human cognition in terms of
grouping similar information together. Compatibility operators such as cardinality …

Traditionally, clustering is the task of dividing samples into homogeneous clusters based on
their degrees of similarity. As samples are assigned to clusters, users need to manually give …

A convolutional neural network (CNN) learning structure is proposed, with added
interpretability-oriented layers, in the form of Fuzzy Logic-based rules. This is achieved by …