This paper is a survey of recent literature in neural networks applications in the field of
automatic control. It is now generally accepted in the field that for nonlinear, imperfectly or …

An application of Neural Networks based Additive Nonlinear Autoregressive Exogenous
(NN-ANARX) structure for modeling and control of nonlinear MIMO systems is presented in …

A state-space technique for control of nonlinear SISO systems identified by an Additive
Nonlinear Autoregressive eXogenous (ANARX) model is presented. Two cases are shown …

In this paper, we present the Additive Nonlinear AutoRegressive Moving Average
(ANARMA) structure as an excellent choice for neural-networks-based inputoutput models …

During the last twenty years a lot of advances in control of complex nonlinear systems and
process have been made. One of the most important reasons for that is development of …

A fast, efficient and new way of real‐time training of dynamic neural networks is presented in
this paper. The proposed method would be suitable for training neural networks for real‐time …

In this paper, we examine data driven modelling procedures for creating a discrete-time
input-output map that can be transformed into an observable state space form. We first …

This paper provides a concise description of a dynamic output feedback linearization
algorithm for control of nonlinear systems by means of Additive Nonlinear Autoregressive …

This paper discusses application of dynamic output feedback linearization algorithm for
adaptive control of nonlinear MIMO systems. Neural Network based Simplified Additive …

In this paper, an application of Neural Networks based Additive Nonlinear AutoRegressive
eXogenous (NN-ANARX) structure is investigated for modeling and control of nonlinear …