Neural Networks Robotic Control (13 results)

Condition: New. pp. 100.

Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

paperback. Condition: New. Language:Chinese.Paperback. Publisher: Higher Education Press. This book from the neural network adaptive control and variable structure control of organic integration perspective. the Department of.

Paperback. Condition: Like New. Like New. book.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 278 | Sprache: Englisch | Produktart: Bücher.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 278 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students. 100 pp. Englisch.

Condition: New. Print on Demand pp. 100 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 100.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Al Ashi MahmoudMahmoud M. Al Ashi, MSc. Had my BEng degree in Telecommunications from City University London in 2007. My MSc degree in Electrical Engineering was obtained from the Islamic University of Gaza in 2014. My research inter.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics.