Fuzziness is ever presented in real life decision making problems. In this paper, we adapt the pessimistic approach to study a pair of linear primal-dual problem under intuitionistic fuzzy (I-fuzzy) environment and prove certain duality results. We generate the duality results using exponential Membership and non-Membership Functions to represent the decision maker's satisfaction and dissatisfaction level. Further, two numerical examples have been given. In each of these illustrations, varying the values of the shape variables in the exponential Membership Functions, various nonlinear optimization problems have been constructed, analyzed and solved. The duality gaps for all these optimization problems have been computed and compared with the duality gap under the linear Membership Function. We found that these gaps for the I-fuzzy linear primal-dual pair under the exponential Membership Functions are smaller as compared with the linear Membership Functions. The main advantage of using the exponential Membership Function is that it has the exibility of altering the values of the shape parameters (as per the decision-maker's satisfaction). Finally, with the help of a suitable ranking defuzzification Function, we have extended our approach to the I-fuzzy linear problem with fuzzy parameters and fuzzy constraints.

Under the additional assumption that the errors are normally distributed, the Ordinary Least Squares (OLS) method is the maximum likelihood estimator. In this paper, we propose, for a simple regression, an estimation method alternative to the OLS method based on a so-called Gaussian Membership Function, one that checks the validity of the verbal explanation suggested by the observer. The fuzzy estimation approach demonstrated here is based on a suitable framework for a natural behavior observed in nature. An application based on a group of MENA countries in 2015 is presented to estimate the employment poverty relationship.

This paper introduces a novel gesture recognition method. In the method, hand trajectory is represented by the sequence of symbols and each symbol has a specific Membership degree obtained from the genetic algorithm training. In order to determine the Membership degree of input observations sequence in a class, the system uses the linear combination of Membership degrees of observations in sequence.Because of using negative and positive samples for training gesture classes in the proposed method, the recognition system has a good performance in distinguishing very similar gestures. Experiments show that the method developed in this study outperforms HMM and SOMM methods in different gesture datasets.

In this paper, a new approach is proposed in order to select an optimal Membership Function for inputs of wind speed prediction system. Then using a fuzzy method and the stochastic characteristics of wind speed in the previous year, the wind speed modeling is performed and the wind speed for the future year will be predicted. In this proposed method, the average and the standard deviation of inputs data are calculated. The Membership Function shape and the domain intervals are evaluated using the variance of system. This technique prevents from trial and error method for defining the shape and domain intervals of optimal Membership Function and helps to achieve the desired prediction in a quick way. The wind speed is estimated in the fuzzy inference system and simulated with the fuzzy logic. The sensitivity analyses are performed by changing the input parameters and Membership Functions shape and the results are compared. The results demonstrate that this new prediction method is a fast and applicable method compared to the other methods since the calculated error will be more than the error of this method if the shape and domain interval of Membership Function are changed.