Reasoning with propositional knowledge based on fuzzy neural logic" (1996). In this article, a new kind of reasoning for propositional knowledge, which is based on the fuzzy neural logic initialed by Teh, is introduced. A fundamental theorem is presented showing that any fuzzy neural logic network can be represented by operations: bounded sum, complement, and scalar product. Propositional calcu...

In this paper we introduce reasoning procedures for ALCQF , a fuzzy description logic with extended qualified quantification. The language allows for the definition of fuzzy quantifiers of the absolute and relative kind by means of piecewise linear functions on N and Q ∩ [0, 1] respectively. In order to reason about instances, the semantics of quantified expressions is defined based on recently...

Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy or vague knowledge. In this paper, we consider the problem of reasoning with fuzzy-EL, which is a fuzzy extension of EL+. We first identify the challenges and present revised completion classification rules for fuzzy-EL that can be handled by MapReduce programs. We then propose an algorithm for sc...

In this work we use the Choquet integral as an aggregation function and we apply it in the fuzzy reasoning method of fuzzy rule-based classification systems. We study the behaviour of several fuzzy measures and we propose a genetic learning method of an appropriate fuzzy measure to model the interaction among the set of rules of each class. In the experimental study we show that the new proposa...

In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum deg...

Higher order singular value decomposition (HOSVD) is an efficient datadriven decomposition technique, and shows the salient ability in the representation of high-dimensional data and feature extraction. In addition, fuzzy reasoning can solve the uncertainties of the source images’ contributions to the fused image and is easy to apply. Motivated by the advantages mentioned above, a new HOSVD and...

This paper presents an approach to similarity based approximate reasoning that elucidates the connection between similarity and existing approaches to inference in approximate reasoning methodology. A set of axioms is proposed to compute a reasonable measure of similarity between two fuzzy sets. Such a measure of similarity is then used at different stages to derive a conclusion in approximate ...

Like other kinds of information, types of objects in the real world are often found to be ®lled with uncertainty and/or partial truth. It may be due to either the vague nature of a type itself or to incomplete information in the process determining it even if the type is crisp, i.e., clearly de®ned. This paper proposes a framework to deal with uncertainty and/or partial truth in automated reaso...

This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple impli...

While knowledge representation languages developed in the context of the Semantic Web build mainly on crisp knowledge, fuzzy reasoning seems to be needed for many applications, e.g. for retrieval of images or other resources. However, techniques for fuzzy reasoning need to scale to large A-Boxes for practical applications. As a first step towards clarifying whether fuzzy reasoning techniques ca...