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Neural Networks in Robotics
. Ed(S): Bekey, George A.; Goldberg, Kenneth Y.
€ 239.76
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Description for Neural Networks in Robotics
Paperback. Editor(s): Bekey, George A.; Goldberg, Kenneth Y. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 575 pages, biography. BIC Classification: PHS; TJFM; UYQ. Category: (G) General (US: Trade). Dimension: 235 x 155 x 30. Weight in Grams: 878.
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 environment. The ability of living systems to learn and to adapt provides ... Read more
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 environment. The ability of living systems to learn and to adapt provides ... Read more
Product Details
Format
Paperback
Publication date
2012
Publisher
Springer-Verlag New York Inc. United States
Number of pages
575
Condition
New
Series
The Springer International Series in Engineering and Computer Science
Number of Pages
563
Place of Publication
New York, NY, United States
ISBN
9781461363941
SKU
V9781461363941
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Neural Networks in Robotics
` Given the potential of neural networks to reduce the design complexity of control systems through learning, this book is essential for those involved in the research and development of robotic control systems. ' University Computing, Vol.15/4, 1993