This paper proposes a fuzzy goal programming based on Taylor series for solving decentralized bilevel multiobjective fractional programming (DBLMOFP) problem. In the proposed approach, all of the membership functions are associated with the fuzzy goals of each objective at the both levels and also the fractional membership functions are converted to linear functions using the Taylor series appr...

A time-fractional heat equation arising in a quiescent medium is established, and its approximate analytical solution obtained by the fractional power series method. The results show that method performs extremely well terms of efficiency simplicity.

This paper investigates the relationship between consumer credits and interest rates in Turkey based on the fractional cointegration approach by using daily observations over the period from 4 January 2002 to 24 December 2010. First, we ignore the possible structural breaks in the series and perform the Geweke and Porter-Hudak (GPH) test on the residuals for fractional cointegration. Second, we...

A mean square error criterion is used in this paper to provide a systematic approach to approximate a long-memory time series by a short-memory ARMA(1; 1) process. Analytic expressions are derived to assess the accuracy of such an approximation. These results are valid not only for the pure fractional noise case, but also for a general autoregressive fractional moving average long-memory time s...

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault...

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we div...