Overview

Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and ...
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Neural Systems for Robotics

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Overview

Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance.

Key Features
* Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology
* Represents the most up-to-date developments in this rapidly growing application area of neural networks
* Contains a new and novel approach to solving Robotics problems

Audience: Researchers and practicioners in the fields of neural networks, robotics, engineering, and vision.

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Product Details

  • ISBN-13: 9780080925097
  • Publisher: Elsevier Science
  • Publication date: 12/2/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 346
  • File size: 7 MB

Meet the Author

Omid Omidvar is a professor of Computer Science at the University of Computer Science at the University of the District of Columbia, Washington, D.C. He is also a technical director of SPPARC center; a supercomputing facility funded by NSF. He received his Ph.D. from the University of Oklahoma in 1967 and has done extensive work in applications of Neural Networks in Optical Character Recognition and Finger Print for the National Institute of Standards and Technology. Dr. Omidvar has been a consultant to many of the world's most important corporations including IBM, Sun, Gumann, and has completed a five year project for the District of Columbia NASA Consortium in design and performance evaluation of neurocontrollers. Dr. Omidvar is also the Editor-in-Chief of the Journal of Artificial Neural Networks, has been an editor of Progress in Neural Network Series since 1990, and has published a large number of journal and conference publications. In addition to teaching, Dr. Omidvar is also currently working as a computer scientist in the Image Recognition Group, Advanced System Division, at NIST.
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Table of Contents

I.E. Dror, M. Zagaeski, D. Rios and C.F. Moss,Neural Network Sonar as a Perceptual Modality for Robotics. W.T. Miller III and A.L. Kun, Dynamic Balance of a Biped Walking Robot. P. van der Smagt and F. Groen,Visual Feedback in Motion. D. DeMers and K. Kreutz-Delgado, Inverse Kinematics of Dextrous Manipulators. Y. Jin, T. Pipe and A. Winfield, Stable Manipulator Trajectory Control Using Neural Networks. P. Gaudiano, F.H. Guenther and E. Zalama, The Neural Dynamics Approach to Sensory-Motor Control. A. Buhlmeier and G. Maneuffel, Operant Conditioning in Robots. B. Hallam, J. Hallam and G. Hayes, A Dynamic Net for Robot Control. Ben Krise and J. van Dam, Neural Vehicles. J. Heikkonen and P. Koikkalainen, Self-Organization and Autonomous Robots.
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