Since its introduction in the mid-sixties (Zadeh, L. A., (1965)), fuzzy set theory has gained recognition in a number of fields in the cases of uncertain, or qualitative or linguistically described system parameters or processes based on approximate reasoning, and has proven suitable and applicable with system describing rules of similar characteristics. It can be successfully applied with nume...

CFNN hybrid system in Multi-sensor data fusion introduced fuzzy logic reasoning and neural network adaptive, self-learning ability, and using fuzzy neurons, so networking skills appropriate to adjust the input and output fuzzy membership function, and can dynamically optimize fuzzy reasoning in global by means of compensated logic algorithm, to make the network more fault tolerance, stability a...

In this paper we describe a fuzzy Description Logic reasoner which implements resolution in order to provide reasoning services for expressive fuzzy DLs. The main innovation of this implementation is the ability to reason over assertions with abstract (unspecified) fuzzy degrees. The answer to queries is, consequently, an algebraic expression involving the (unknown) fuzzy degrees and the degree...

A variety of fuzzy description logics are proposed to extend classical description logics with fuzzy capability. However, reasoning with general TBoxes is still an open problem in fuzzy description logics. In this paper, we present a novel discrete tableau algorithm for a given fuzzy description logic FSHI with general TBoxes, which tries to construct discrete tableaus of FSHI knowledge bases. ...

Fuzzy Description Logics (DLs) with t-norm semantics have been studied as a means for representing and reasoning with vague knowledge. Recent work has shown that even fairly inexpressive fuzzy DLs become undecidable for a wide variety of t-norms. We complement those results by providing a class of t-norms and an expressive fuzzy DL for which ontology consistency is linearly reducible to crisp r...

An approach based on interval belief degrees and fuzzy evidential reasoning analytical algorithm is developed for multi-criteria decision problems with uncertainties. The criteria weights are represented by interval numbers and criteria values by triangular intuitionistic fuzzy numbers. The proposed approach does not need to utilize the theories such as arithmetic operations for Triangular intu...

Case-based reasoning is a recognized paradigm and has been explored in both applied and methodological directions. In the several phases of CBR, the adaptation phase is certainly the most problematic whereas the most characteristic and interesting phase. We propose to view this task through a fuzzy analogical scheme. The adaptation is realized by focusing on the relation existing between the pr...

Fuzzy logic and case-based reasoning are leading methodologies for the application of artificial intelligence. We describe a system, WIND-1, that combines fuzzy logic and casebased reasoning to produce forecasts of airport cloud ceiling and visibility. Knowledge about meteorological and temporal features that experienced forecasters use to construct analogous climatological scenarios is encoded...

Sophisticated uncertainty representation and reasoning are necessary for the alignment and integration of Web data from different sources. For this purpose the extension of the Description Logics using fuzzy set theory has been proposed, resulting to fuzzy Description Logics (DLs). However, despite the fact that since the initial proposal a lot of work has been done in the area, the practicabil...

Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systemswhen applying them to th...