В книге дается систематическое изложение теории эффективных последовательных
алгоритмов многомерной оптимизации и оценивания, дающих состоятельные оценки …

A presentation of techniques in advanced process modelling, identification, prediction, and
parameter estimation for the implementation and analysis of industrial systems. The authors …

Presents a number of new and potentially useful self-learning (adaptive) control algorithms
and theoretical as well as practical results for both unconstrained and constrained finite …

Abstract In Chap. 2, further to Introduction, two approaches in controller tuning, parametric
and non-parametric, are considered. Non-parametric methods of tuning based on the …

The application of fuzzy reasoning techniques and neural network structures to model-
based predictive control (MPC) is studied. First, basic structures of MPC algorithms are …

Nonlinear model predictive control (MPC) of a simulated chaotic cutting process is
presented. The nonlinear MPC combines a neural-network model and a genetic-algorithm …

In this paper, a new control technique for nonlinear control based on hybrid neural modeling
is proposed. For neural network training, a variant of the well-known gradient steepest …

A new simple solution of deriving adaptive PID control based on artificial neural network
(ANN) policy is presented. The design problem consists of training of both, a recurrent and a …

Most of the controller tuning methods in process control are based on linear models.
Through the development of a detail model of the actuator-pneumatic valve dynamics, we …

This contribution proposes a modified version of the Iterative Dynamic Programming (IDP)
method. Two main differences to the original method are introduced. The new algorithm …