Yager [ 1 ] introduced the concept of q- rung orthopair fuzzy sets ( q- ROFSs) in which the sum of the q ^th exponent of the support for membership and the q ^th exponent of the support against membership is bounded by one. Thus, the q- ROFSs are an important way to express uncertain information in broader space, and they are superior to the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs). However, in dealing with many real life situations, it is not appropriate for experts to precisely quantify their judgements with a crisp number due to insufficiency in available information. In such situation it is advisable for decision makers to provide their judgements by the subset of the closed interval [0, 1]. The notion of interval-valued q- rung orthopair fuzzy sets (IV q- ROFSs) is presented in this paper, which allows decision makers to provide their satisfying degrees and non-satisfying degrees to a given set of alternatives by an interval value. Some of its important operations such as: negation, union and intersection are also given. Based on these operations, the aggregation of IV q- ROFSs is also studied.

Fuzzy time series models are of great interest in forecasting when the information is imprecise and vague. However, the major problem in fuzzy time series forecasting is the accuracy of the forecasted values. In the present study we propose a hybrid method of forecasting based on fuzzy time series and intuitionistic fuzzy sets. The proposed model is a simplified computational approach that uses the degree of nondeterminacy to establish fuzzy logical relations on time series data. The developed model was implemented on the historical enrollment data for the University of Alabama and the forecasted values were compared with the results of existing methods to show its superiority. The suitability of the proposed method was also examined in forecasting market share prices of the State Bank of India on the Bombay Stock Exchange, India.

In this paper, policy iteration (PI) based adaptive critic approach has been utilized to design a controller for AGC scheme. PI approach consists policy evaluation and improvement steps to determine optimal control. Optimal controller based on linear quadratic regulator (LQR) design requires complete knowledge of the system dynamics, whereas PI technique provides optimal control of system without knowing the internal dynamics. PI based optimal controller has been tested on a power system having two area deregulated. The results are used to show the effective performance of the designed controller. A comparative study has also been given in between the performance of the PI based optimal controller and LQR.

In this paper, a deregulated LFC scheme is considered. For successful operation of LFC scheme, remote measurements are transmitted to control center consisting delays. The existence of delays affects the performance of frequency control scheme and at worst can causes instability. Therefore, a better control scheme the must encounter the effects of transmission/communication delays. In this study, a test system is considered by two-area thermal system. GA based PID controller has been designed to settle down the frequency deviations in prescribed limit at steady state. The performance of the proposed controller is evaluated under various communication delays. The obtained results show the validity and success of the designed controller.

The concept of q‐rung orthopair fuzzy set (q‐ROFS) is the extension of intuitionistic fuzzy set (IFS) in which the sum of the qth power of the support for and the qth power of the support against is bounded by one. Therefore, the q‐ROFSs are an important way to express uncertain information in broader space, and they are superior to the IFSs and the Pythagorean fuzzy sets. In this paper, the familiarity degree of the experts with the evaluated objects is incorporated to the initial assessments under q‐rung orthopair fuzzy environment. For this, some aggregation operators are proposed to combine these two types of information. Their some important properties are also well proved. Furthermore, these developed operators are utilized in a multicriteria decision‐making approach and demonstrated with a real life problem of customers' choice. Then, the experimental results are compared with other existing methods to show its superiority over recent research works.

It has been seen in literature that the notion of intuitionistic fuzzy sets (IFSs) is very powerful tool to deal with real life problems under the environment of uncertainty. This notion of IFSs favours the intermingling of the uncertainty index in membership functions. The uncertainty index is basically generated from a lot of parameters such as lack of awareness, historical information, situation, short of standard terminologies, etc. Hence, the uncertainty index appended finding the membership grade under IFSs needs additional enhancement. Then, the concept of a moderator intuitionistic fuzzy set (MIFS) is defined by adding a parameter in the IFSs environment to make the uncertain behaviour more accurate. In this chapter, some new moderator intuitionistic fuzzy hybrid aggregation operators are presented on the basis of averaging and geometric point of views to aggregate moderator intuitionistic fuzzy information. Then, a multi-criteria decision-making (MCDM) approach is provided and successfully implemented to real-life problems of candidate selection.