The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in...

abstract in this paper, a fuzzy pid with new structure is proposed to solve the load frequency control in interconnected power systems. in this study, a new structure and effective of the fuzzy pid-type load frequency control (lfc) is proposed to solve the load frequency control in interconnected power systems. the main objective is to eliminate the deviations in the frequency of different area...

Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, wheth...

A controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership...

six-sigma has some measures which measure performance characteristics related to a process. in most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. in this paper, to estimate some of these measures, including defects per million opportunities (dpmo), defects per opportunity (dpo), defects per unit (dpu) and yield, a new algorithm ba...

Received Jan 9, 2018 Revised Mar 2, 2018 Accepted Mar 18, 2018 This paper gives an overview of Type-2 Fuzzy sets (T2FSs) and Type-2 fuzzy Logic system (T2FLS) considering one aviation scenario. The existing type-1 Fuzzy system has limited capability to handle the uncertainty directly. In order to overcome the limitations of Type-1 fuzzy Logic system (T1FLS), a next level of fuzzy set is introdu...

in this work, we introduce notion of connectedness on fuzzy soft topological spaces and present fundamentals properties. we also investigate effect to fuzzy soft connectedness. moreover, c_i-connectedness which plays an important role in fuzzy topological space extend to fuzzy soft topological spaces.

Interval Type-2 Fuzzy TOPSIS (IT2FTOPSIS) is a useful way to handle Fuzzy Multiple Attribute DecisionMaking (FMADM) problems in a more flexible and intelligent manner. It is very useful due to the fact that it uses Type-2 Fuzzy Sets (T2FSs) rather than Type-1 Fuzzy Sets (T1FSs) to represent the evaluating values and the weights of attributes. Besides, all the linguistic terms are pointed in Typ...

in this work, we define a fuzzy soft set theory and its related properties. we then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.