Modelling dynamic systems with artificial neural networksand related methods

Modelling Dynamic Systems with Artificial Neural Networks and Related Methods can be used as a textbook for the field it covers or as an introductory textbook for more advanced literature in the field. The textbook comprises introduction to artificial neural networks, identification of linear and nonlinear dynamic systems, control with artificial neural networks, local-model networks and blended multiple-model systems, design of gain-scheduling control and identification of nonlinear systems with Gaussian processes. It is intended for undergraduate students, especially at the postgraduate level, who have sufficient knowledge in dynamic systems, as well as for professionals who wish to familiarise themselves with the concepts and views described. The textbook is not intended to be a detailed theoretically based description of the subject, but rather an overview of the field of identification of dynamic systems with neural networks and related methods from the perspective of systems theory and, in particular, its application. The work is intended to inform the reader about the views on this subject, which are related but treated very differently

Zbirka nalog iz avtomatskega vodenja sistemov

Zbirka nalog iz avtomatskega vodenja sistemov so gradivo, ki naj olajša spremljanje predavanj in študij predmeta Avtomatsko vodenje sistemov na drugi stopnji programa Gospodarski inženiring Poslovno-tehniške fakultete na Univerzi v Novi Gorici

Modelling and control of dynamic systems using gaussian process models

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control