Behavior Learning Constructive Neural by Jun (6 results)

Taschenbuch. Condition: Neu. Behavior Learning with Constructive Neural Networks in Mobile Robotics | Robot Behavior Learning: Algorithms and Experiments | Jun Li | Taschenbuch | 156 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783838380063 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In behavior-based robotics, a robot achieves a required task by using various behaviors as the building blocks for that overall task. A robot behavior in turn is a sequence of sensory states and their corresponding motor actions, and extends in time and space. Making a robot able to learn (or develop) meaningful and purposeful behaviors from its own experiences has played one of the most important roles in intelligent robotics, and have been called the hallmark of intelligence. This book presents a learning system for acquiring robot behaviors by mapping sensor information directly to motor actions. It addresses the integration of three learning paradigms, namely unsupervised learning, supervised learning, and reinforcement learning. The approach is characterized by the use of constructive artificial neural networks, Several novel techniques for robot learning using constructive radial basis function networks are introduced. The learning system is verified by a number of experiments involving a real robot learning different behaviors. It is shown that the learning system is useful as a generic learning component for acquiring diverse behaviors in mobile robots. 156 pp. Englisch.

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Li JunDr. Li Jun is currently with the College of Automation, Chongqing University, China. He received his PhD degree from the Center for Applied Autonomous Sensor Systems, Oerebro University, Sweden. His research interests include le.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In behavior-based robotics, a robot achieves a required task by using various behaviors as the building blocks for that overall task. A robot behavior in turn is a sequence of sensory states and their corresponding motor actions, and extends in time and space. Making a robot able to learn (or develop) meaningful and purposeful behaviors from its own experiences has played one of the most important roles in intelligent robotics, and have been called the hallmark of intelligence. This book presents a learning system for acquiring robot behaviors by mapping sensor information directly to motor actions. It addresses the integration of three learning paradigms, namely unsupervised learning, supervised learning, and reinforcement learning. The approach is characterized by the use of constructive artificial neural networks, Several novel techniques for robot learning using constructive radial basis function networks are introduced. The learning system is verified by a number of experiments involving a real robot learning different behaviors. It is shown that the learning system is useful as a generic learning component for acquiring diverse behaviors in mobile robots.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In behavior-based robotics, a robot achieves a required task by using various behaviors as the building blocks for that overall task. A robot behavior in turn is a sequence of sensory states and their corresponding motor actions, and extends in time and space. Making a robot able to learn (or develop) meaningful and purposeful behaviors from its own experiences has played one of the most important roles in intelligent robotics, and have been called the hallmark of intelligence. This book presents a learning system for acquiring robot behaviors by mapping sensor information directly to motor actions. It addresses the integration of three learning paradigms, namely unsupervised learning, supervised learning, and reinforcement learning. The approach is characterized by the use of constructive artificial neural networks, Several novel techniques for robot learning using constructive radial basis function networks are introduced. The learning system is verified by a number of experiments involving a real robot learning different behaviors. It is shown that the learning system is useful as a generic learning component for acquiring diverse behaviors in mobile robots.