Neural network with a specific restricted connectivity structure is used to identify a model of a
real-life process. Parameters of the identified model are used to design a controller based …

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are
introduced in the article. This system is basically used for time series forecasting. This …

Abstract Model predictive control (MPC) uses a model of the system as a proxy to obtain
multi-step predictions of the state or output variables. Thus MPC performance significantly …

This paper explores the utilization of machine learning techniques to develop an
approximate input-output linearizable neural network model aimed at improving disturbance …

This paper is devoted to application of artificial Neural Network based Simplified Additive
Autoregressive exogenous model for identification and control of a liquid level tank system …

This paper presents a data-based approach to force control. For this purpose, representative
data of the robot in operation is recorded to model it afterwards by methods of time series …

Choice of the architecture of the neural network makes it possible to find its optimal structure
for the control of nonlinear multi-input multi-output (MIMO) systems using the linearization …

This paper raises the issue of finding reduced/minimal state-space form for MIMO systems
based on neural networks. Two cases are studied: when system is given as a “black-box” …

В статье проведен анализ использования в задачах экологического мониторинга
традиционных нейронных сетей с фиксированной архитектурой и МГУА-сетей (метод …

In this paper two computational intelligence methods are considered. In the first one the
Neural Network based Controller with combination of Genetic Algorithm network structure …