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BL
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| Record Number
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846523
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| Main Entry
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Arana-Daniel, Nancy
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| Title & Author
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Neural networks for robotics : : an engineering perspective /\ Nancy Arana-Daniel, Carlos Lopez-Franco, Alma Y. Alanis.
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| Publication Statement
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Boca Raton, FL :: CRC Press/Taylor & Francis Group,, [2019]
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| Page. NO
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1 online resource (229 pages)
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| ISBN
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1351231774
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: 1351231782
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: 1351231790
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: 9781351231770
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: 9781351231787
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: 9781351231794
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0815378688
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9780815378686
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| Notes
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6.5 Visual Servo Control
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| Contents
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Cover; Half Title; Title; Copyright; Dedication; Contents; Preface; Abbreviations; Chapter 1 Recurrent High Order Neural Networks for Rough Terrain Cost Mapping; 1.1 Introduction; 1.1.1 Mapping background; 1.2 Recurrent High Order Neural Networks, RHONN; 1.2.1 RHONN order; 1.2.2 Neural network training; 1.2.2.1 Kalman lter; 1.2.2.2 Kalman lter training; 1.2.2.3 Extended Kalman lter-based training algorithm, EKF; 1.3 Experimental Results: Identi cation of Costs Maps Using RHONNs; 1.3.1 Synthetic dynamic environments; 1.3.1.1 Synthetic dynamic random environment number 1
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1.3.1.2 Synthetic dynamic random environment number 21.3.1.3 Synthetic dynamic random environment number 3; 1.3.2 Experiments using real terrain maps; 1.3.2.1 Real terrain map: grove environment; 1.3.2.2 Real terrain map: golf course; 1.3.2.3 Real terrain map: forest; 1.3.2.4 Real terrain map: rural area; 1.4 Conclusions; Chapter 2 Geometric Neural Networks for Object Recognition; 2.1 Object Recognition and Geometric Representations of Objects; 2.1.1 Geometric representations and descriptors of real objects; 2.2 Geometric Algebra: An Overview; 2.2.1 The geometric algebra of n-D space
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2.2.2 The geometric algebra of 3-D space2.2.3 Conformal geometric algebra; 2.2.4 Hyperconformal geometric algebra; 2.2.5 Generalization of G6; 3 into G2n; n; 2.3 Cli ord SVM; 2.3.1 Quaternion valued support vector classi er; 2.3.2 Experimental results; 2.4 Conformal Neuron and Hyper-Conformal Neuron; 2.4.1 Hyperellipsoidal neuron; 2.4.2 Experimental results; 2.5 Conclusions; Chapter 3 Non-Holonomic Robot Control Using RHONN; 3.1 Introduction; 3.2 RHONN to Identify Uncertain Discrete-Time Nonlinear Systems; 3.3 Neural Identi cation; 3.4 Inverse Optimal Neural Control
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3.5 IONC for Non-Holonomic Mobile Robots3.5.1 Robot model; 3.5.2 Wheeled robot; 3.5.2.1 Controller design; 3.5.2.2 Neural identi cation of a wheeled robot; 3.5.2.3 Inverse optimal control of a wheeled robot; 3.5.2.4 Experimental results; 3.5.3 Tracked robot; 3.5.3.1 Controller design; 3.5.3.2 Results; 3.6 Conclusions; Chapter 4 NN for Autonomous Navigation on Non-Holonomic Robots; 4.1 Introduction; 4.2 Simultaneous Localization and Mapping; 4.2.1 Prediction; 4.2.2 Observations; 4.2.3 Status update; 4.3 Reinforcement Learning; 4.4 Inverse Optimal Neural Controller
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4.4.1 Planning-Identi er-Controller4.5 Experimental Results; 4.6 Conclusions; Chapter 5 Holonomic Robot Control Using Neural Networks; 5.1 Introduction; 5.2 Optimal Control; 5.3 Inverse Optimal Control; 5.4 Holonomic Robot; 5.4.1 Motor dynamics; 5.4.2 Neural identi cation design; 5.4.3 Control design; 5.4.4 Omnidirectional mobile robot kinematics; 5.5 Visual Feedback; 5.6 Simulation; 5.7 Conclusions; Chapter 6 Neural Network-Based Controller for Unmanned Aerial Vehicles; 6.1 Introduction; 6.2 Quadrotor Dynamic Modeling; 6.3 Hexarotor Dynamic Modeling; 6.4 Neural Network-Based PID
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| Subject
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Neural networks (Computer science)
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| Subject
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Robots-- Control systems.
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Neural networks (Computer science)
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Robots-- Control systems.
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| Subject
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.8/92632
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| LC Classification
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TJ211.35.A73 2019
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| Added Entry
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Alanis, Alma Y.
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Lopez-Franco, Carlos.
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