The present paper emphasises on equi-statistical convergence, pointwise statistical convergence and uniform statistical convergence for a sequence of real-valued functions by using deferred Cesàro and deferred Euler statistical convergence and obtain various implicative results with supporting examples. We make an effort to demonstrate Korovkin-type approximation theorem via deferred Cesàro and deferred Euler equi-statistical convergence. We also present an example which shows that our Korovkin-type theorem is powerful than its classical version. Further, we study rates of deferred Cesàro and deferred Euler equi-statistical convergence via modulus of continuity.

In this paper we characterize the compact, invertible and Fredholm multiplication operators on Cesàro sequence spaces.

An adaptive hybrid neural control scheme is presented for uncertain non-linear discrete-time Systems (UNLDTS) with non-symmetric dead-zone input and unknown disturbances. This work aims to design an efficient control scheme for the proposed systems in the case of non-symmetric dead-zone as the input due to the presence of which the control of such systems becomes very complex and difficult. The system is converted into an n-step ahead predictor and an adaptive compensative term is introduced to overcome the non-symmetric dead-zone present in the system. A hybrid neural network controller is constructed for the control of the proposed class of systems. The designed controller is proved to be semi-globally uniformly ultimately bounded with the assistance of Lyapunov theory and the error is proved to approach very close to zero. The effectiveness of the proposed scheme is validated through two simulation examples and one example has been inspired from a real word system named continuous stirred tank reactor taken in discrete-time form which ensures the applicability of this controller in real-world applications.