Neural Network Control of Robots and Nonlinear Systems, F W Lewis, S. Jagannathan , A Yesildirak
Автор: Heidar A. Talebi; Farzaneh Abdollahi; Rajni V. Pat Название: Neural Network-Based State Estimation of Nonlinear Systems ISBN: 1441914374 ISBN-13(EAN): 9781441914378 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text offers neural network schemes for state estimation, system identification and fault detection. It covers mathematical proof of stability, experimental evaluation, and robustness against unmolded dynamics, external disturbances and measurement noises.
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering.
Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields
Offers instructive examples and simulations, including source codes
Provides the basic architecture of control science and engineering
Автор: Jinkun Liu Название: Radial Basis Function (RBF) Neural Network Control for Mechanical Systems ISBN: 3642348157 ISBN-13(EAN): 9783642348150 Издательство: Springer Рейтинг: Цена: 156720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces concrete design methods and MATLAB simulations of stable adaptive Radial Basis Function (RBF) neural control strategies. It presents a broad range of implementable neural network control design methods for mechanical systems.
Автор: Jinkun Liu Название: Radial Basis Function (RBF) Neural Network Control for Mechanical Systems ISBN: 364243455X ISBN-13(EAN): 9783642434556 Издательство: Springer Рейтинг: Цена: 139310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces concrete design methods and MATLAB simulations of stable adaptive Radial Basis Function (RBF) neural control strategies. It presents a broad range of implementable neural network control design methods for mechanical systems.
Автор: Kenneth J. Hunt; George R. Irwin; Kevin Warwick Название: Neural Network Engineering in Dynamic Control Systems ISBN: 1447130685 ISBN-13(EAN): 9781447130680 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, ....
Автор: Ding, Derui (department Of Control Science And Eng Название: Performance analysis and synthesis for discrete-time stochastic systems with network-enhanced complexities ISBN: 1138610011 ISBN-13(EAN): 9781138610019 Издательство: Taylor&Francis Рейтинг: Цена: 188850.00 T Наличие на складе: Нет в наличии. Описание: This book aims to provide a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics.
Автор: Venkatesan, Ragav, Название: Convolutional Neural Networks In Vi ISBN: 1498770398 ISBN-13(EAN): 9781498770392 Издательство: Taylor&Francis Рейтинг: Цена: 168430.00 T Наличие на складе: Невозможна поставка. Описание: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner`s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems.
Автор: Tiumentsev, Yury Название: Neural Network Modeling and Identification of Dynamical Systems ISBN: 0128152540 ISBN-13(EAN): 9780128152546 Издательство: Elsevier Science Рейтинг: Цена: 132500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
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 for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft.
Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training
Offers application examples of dynamic neural network technologies, primarily related to aircraft
Provides an overview of recent achievements and future needs in this area
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
