In recent years, much attention have been given to develop a class of two-step and three-step iterative methods for solving the variational inclusions and inequalities using the technique of updating the solution.
Runge-Kutta methods are a class of one step methods which gain their efficacy by computing intermediate approximations to the solution within a step.
These facts motivated us to study a class of three-step iterative schemes in the setting of (operatorname{CAT} k)) spaces with (k>0).
The authors in [18] proposed a class of multi-step fixed point proximity algorithms, including several existing algorithms as special examples, for example the algorithms in [11, 12].
In 2006, Plubtieng et al. [12] studied a class of three-step iterative scheme, for three asymptotically nonexpansive mappings, in a uniformly convex Banach space satisfying Opial's condition.
A class of the fractional multi-step method is proposed by Lubich [35] and Galeone and Garrappa [36], the fractional Adams method is proposed by Diethelm et al. [37] and Odibat and Momani [3] and Grünwald-Letnikov approximation based on the Grünwald-Letnikov definition of the fractional derivative is addressed in [38] and discussed the analysis of convergence and stability.
In this paper, the robust recursive filtering problem is studied for a class of uncertain systems with finite-step correlated noises, stochastic nonlinearities and autocorrelated missing measurements.
DURING a recent session with a class of 3-year-olds at Stepping Stones, a special education preschool here, Angeline Brown, a music therapist, strummed her guitar and led the group in song.
In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts.
The optimal filtering problem is investigated for a class of discrete stochastic systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements.
