Artificial Neural Networks for Modelling and Control of Non-Linear 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 Artificial Neural Networks for Modelling and Control of Non-Linear Systems write by Johan A.K. Suykens. This book was released on 2012-12-06. Artificial Neural Networks for Modelling and Control of Non-Linear Systems available in PDF, EPUB and Kindle. Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
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.
Sampled-Data Models for Linear and Nonlinear Systems
Sampled-Data Models for Linear and Nonlinear 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 Sampled-Data Models for Linear and Nonlinear Systems write by Juan I. Yuz. This book was released on 2013-10-17. Sampled-Data Models for Linear and Nonlinear Systems available in PDF, EPUB and Kindle. Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives: · the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable; · although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and · the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central. The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications. Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system.
Adaptive Learning Methods for Nonlinear System Modeling
Adaptive Learning Methods for Nonlinear System Modeling - 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 Adaptive Learning Methods for Nonlinear System Modeling write by Danilo Comminiello. This book was released on 2018-06-11. Adaptive Learning Methods for Nonlinear System Modeling available in PDF, EPUB and Kindle. Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Data-Driven Science and Engineering
Data-Driven Science and Engineering - 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-Driven Science and Engineering write by Steven L. Brunton. This book was released on 2022-05-05. Data-Driven Science and Engineering available in PDF, EPUB and Kindle. A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
