The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster involving non-crisp numbers, in this paper. This achieved by a vectorized representation of distance, between two numbers like the crisp case. Using proposed method, named vector (VFCSS), all and can be clustered FCSS algorithm unique structure. Therefore, we implement over various types structure with only...

Control charts are widely used in industrial processes as well as in health sciences and particularly for monitoring the performance of cardiac surgeon or a group of surgeons based on the preoperative risk of patients. Since the preoperative risk is a vague and nonprecise variable and the anesthesiologists after checking how many risk factors a patient has, determine the risk of mortality befor...

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular ...

This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFCN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and in7uence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFCN clustering algorithm is more ro...

Machine learning algorithms are commonly employed in landslide susceptibility assessments. Recently, that utilize artificial intelligence have come into prominence. This study attempts to adapt the most fundamental framework of deep and introduces fuzzy theory concepts analyze while updating network parameters with trial-and-error methods. The final analysis results will compare those logistic ...

We present new tools for fuzzy arithmetic with fuzzy numbers, based on the parametric representation of fuzzy numbers and new fuzzy operations, the generalized difference and the generalized division of fuzzy numbers. The new operations are described in terms of the parametric LR and LU representations of fuzzy numbers and the corresponding algorithms are described. An application to the soluti...

Single-period problem (SPP) is a classical stochastic inventory model that has become very popular recently. In this research, we developed a SPP with fuzzy environment. The demand of each product is considered as LR-fuzzy variables (ranking fuzzy numbers based on the left and right deviation degrees), and multiple constraints (including service level, batch order, budget, space and upper limit...

A b s t r a c t A neural network for classifcation problems with linguistic terms is proposed. A fuzzy input is represented as a LR-type fizzy set. A generalized pocket algorithm, called f izzy pocket algorithm, that utilizes .IR-type fuzzy sets operations and defuzziJication method is first applied to train a linear threshold unit ( LTU ). This LTU node will classrfj, as many fuzzy input insta...