This paper discusses on granular computing and the role of inexactness in its process. It will discuss on emergence of granular computing and consider fuzzy sets. Fuzzy set is considered due to ability of dealing with uncertainties, where f.ggeneralization has intersected for fuzzy and granular computing. Linguistic variables are discussed due to their utilizations for labeling fuzzy granules.

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency. The empirical data used in our investigation are five-minute mid-price time series from FX markets. The ANFIS optimisation involves back-testing as well as varying the number of epochs...

By connecting the fuzzy weighted average (FWA) and the generalized centroid (GC) of an interval type-2 fuzzy set we have arrived at a new αcut algorithm for solving the FWA problem, one that is monotonically and super-exponentially convergent. Our new algorithm uses the Karnik-Mendel (KM) algorithms to compute the FWA α-cut end-points. No faster algorithm for solving this problem exists to-date.

The research aims to prioritize the solutions overcome challenges of Lean Six Sigma 4.0 (LSS 4.0). It is an integrated approach with lean, six sigma, and Industry attributes. This helps achieve organizational excellence sustainable development goals. Fuzzy stepwise weight assessment ratio analysis (fuzzy-SWARA) was used estimate weights LSS challenges. Furthermore, fuzzy-weighted aggregated sum...

Skeletonization provides a compact, yet effective representation of an object. Despite limited resolution, most medical imaging applications till date use binary skeletonization which is always associated with thresholding related data loss. A recently-developed fuzzy skeletonization algorithm directly operates on fuzzy objects in the presence of partially volumed voxels and alleviates this dat...

Since the T-S fuzzy system can approximate any nonlinear system with arbitrary accuracy, it is also expected to be a suitable approach to observe the states of a stochastic nonlinear system. Up to date, a few state estimators for stochastic T-S fuzzy systems have been proposed and applied to various fields without rigorous proof. In this paper, we first derive a sufficient condition based on th...

In recent years answer set programming has been extended to deal with multi-valued predicates. The resulting formalisms allows for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining the stable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where many efficient solvers have been cons...

Classical ontologies are not suitable to represent imprecise nor uncertain pieces of information. Fuzzy Description Logics were born to represent the former type of knowledge, but they require an appropriate fuzzy language to be agreed and an important number of available resources to be adapted. An alternative is to use classical ontologies to represent fuzzy ontologies. To date, all of the wo...