On a new faster implicit fixed point iterative scheme in convex metric spaces

The purpose of this paper is to consider a new implicit iteration and study its strong convergence,stability,and data dependence. it is proved through numerical examples that newly introduced iteration has better convergence rate than well known implicit mann iteration as well as implicit ishikawa iteration and implicit iterations converge faster as compared to corresponding explicit iterations. applications of implicit iterations to rnn (recurrent neural networks) analysis are also presented. © 2015 renu chugh et al.