Modelling and Control of Dynamic Systems Using Gaussian Process Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modelling and Control of Dynamic Systems Using Gaussian Process Models write by Juš Kocijan. This book was released on 2015-11-21. Modelling and Control of Dynamic Systems Using Gaussian Process Models available in PDF, EPUB and Kindle. 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.
Modelling and Parameter Estimation of Dynamic Systems
Modelling and Parameter Estimation of Dynamic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modelling and Parameter Estimation of Dynamic Systems write by J.R. Raol. This book was released on 2004-08-13. Modelling and Parameter Estimation of Dynamic Systems available in PDF, EPUB and Kindle. This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Efficient Reinforcement Learning Using Gaussian Processes
Efficient Reinforcement Learning Using Gaussian Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Reinforcement Learning Using Gaussian Processes write by Marc Peter Deisenroth. This book was released on 2010. Efficient Reinforcement Learning Using Gaussian Processes available in PDF, EPUB and Kindle. This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Innovations in Intelligent Machines-5
Innovations in Intelligent Machines-5 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Innovations in Intelligent Machines-5 write by Valentina Emilia Balas. This book was released on 2014-05-22. Innovations in Intelligent Machines-5 available in PDF, EPUB and Kindle. This research monograph presents selected areas of applications in the field of control systems engineering using computational intelligence methodologies. A number of applications and case studies are introduced. These methodologies are increasing used in many applications of our daily lives. Approaches include, fuzzy-neural multi model for decentralized identification, model predictive control based on time dependent recurrent neural network development of cognitive systems, developments in the field of Intelligent Multiple Models based Adaptive Switching Control, designing military training simulators using modelling, simulation, and analysis for operational analyses and training, methods for modelling of systems based on the application of Gaussian processes, computational intelligence techniques for process control and image segmentation technique based on modified particle swarm optimized-fuzzy entropy.
Data-based Modelling of Nonlinear Systems for Control
Data-based Modelling of Nonlinear Systems for Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-based Modelling of Nonlinear Systems for Control write by Gregor Gregorcic. This book was released on 2004. Data-based Modelling of Nonlinear Systems for Control available in PDF, EPUB and Kindle. This work presented here, investigates in depth the techniques for modelling of unknown nonlinear dynamic systems from their observed input-output behaviour. The research focuses on the type of models which can be applied to model-based nonlinear control strategies. Local model networks are discussed and compared with radical basis networks and Takagi-Sugeno fuzzy models. Issues such as the importance of the choice of the scheduling variable, the problem of off-equilibrium dynamics and the cruse of dimensionality are addressed. A discussion about the difference between interpolation techniques between local models is given. The model based nonlinear control strategies based on the local models network are presented and compared with pole-placement adaptive control. The Gaussian process prior approach as a nonparametric Bayesian alternative to modelling of the nonlinear systems from data is presented. The advantage of the availability of measure of model uncertainty is explained. It is shown how the Gaussian process model relates to parametrical models and particular to the radial basis function network. The nonlinear internal model control structure was extended by utilising the Gaussian process model, where the uncertainty of the model was incorporated into the numerical inversion algorithm to help improve the closed-loop performance. A novel modelling technique combining the advantages of local model networks and Gaussian processes was developed. A linear Gaussian process model as a building block of a local linear Gaussian process model network was proposed. A structure identification procedure was provided and a structure optimisation algorithm, utilising a minimisation of the network uncertainty was developed. A variety of case studies are provided to support the work presented here. The continuous stirred tank reactor was used to demonstrate the application of local model networks. Two nonlinear systems were modelled from real data. First a hydraulic position system was modelled using the Gaussian process technique and then a nonlinear model of a laboratory-scale process rig was identified using the local linear Gaussian process network modelling approach.
