Neural Network Modeling and Identification of Dynamical Systems

• Author : Yuri Tiumentsev,Mikhail Egorchev
• Publisher : Academic Press
• Release Date : 2019-05-17
• ISBN : 9780128154304

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions

• Author : M. Norgaard
• Publisher : Unknown
• Release Date : 2003
• ISBN : OCLC:876537456

• Author : George A. Bekey,Kenneth Y. Goldberg
• Publisher : Springer Science & Business Media
• Release Date : 2012-12-06
• ISBN : 9781461531807

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the

• Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
• Publisher : Academic Press
• Release Date : 2020-01-15
• ISBN : 9780128170793

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on

• Author : Kazumi Nakamatsu,Jair Minoro Abe
• Publisher : IOS Press
• Release Date : 2005
• ISBN : 9781586035686

LAPTEC2005 promoted the discussion and interaction between researchers and practitioners focused on both theoretical and practical disciplines concerning logics applied to technology, with diverse backgrounds including all kinds of intelligent systems having classical or non-classical logics as underlying common matters. It was the first time for LAPTEC to be held in a different country than Brazil since its birth in 2000, and this has made the congress more international. This book is dedicated to Emeritus Professor Atsuyuki Suzuki in commemoration of

• Author : Boris Kryzhanovsky,Witali Dunin-Barkowski,Vladimir Redko,Yury Tiumentsev
• Publisher : Springer Nature
• Release Date : 2019-09-03
• ISBN : 9783030304256

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXI International Conference on Neuroinformatics, held

• Author : Anonim
• Publisher : Academic Press
• Release Date : 1998-02-09
• ISBN : 0080553907

The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal

• Author : Bipin Kumar Tripathi
• Publisher : Springer
• Release Date : 2014-11-05
• ISBN : 9788132220749

The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty

• Author : Shahar Dror,Daniel Joseph Collins,Naval Postgraduate School (U.S.)
• Publisher : Unknown
• Release Date : 1992
• ISBN : OCLC:34297543

• Author : Oliver Nelles
• Publisher : Springer Science & Business Media
• Release Date : 2013-03-09
• ISBN : 9783662043233

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

• Author : Zhang, Ming
• Publisher : IGI Global
• Release Date : 2010-02-28
• ISBN : 9781615207121

"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.

• Author : Wojciech Mitkowski,Janusz Kacprzyk,Krzysztof Oprzędkiewicz,Paweł Skruch
• Publisher : Springer
• Release Date : 2017-06-06
• ISBN : 9783319606996

This volume contains the proceedings of the KKA 2017 – the 19th Polish Control Conference, organized by the Department of Automatics and Biomedical Engineering, AGH University of Science and Technology in Kraków, Poland on June 18–21, 2017, under the auspices of the Committee on Automatic Control and Robotics of the Polish Academy of Sciences, and the Commission for Engineering Sciences of the Polish Academy of Arts and Sciences. Part 1 deals with general issues of modeling and control, notably flow modeling and control, sliding

• Author : Zhang, Ming
• Publisher : IGI Global
• Release Date : 2021-02-05
• ISBN : 9781799835653

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted

• Author : Leszek Rutkowski,Janusz Kacprzyk
• Publisher : Springer Science & Business Media
• Release Date : 2003-02-12
• ISBN : 3790800058

This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of

• Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
• Publisher : Academic Press
• Release Date : 2019-03-15
• ISBN : 9780128182475

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the