Neural Networks for Robotics : (Record no. 72271)

000 -LEADER
fixed length control field 04098nam a2200505Ii 4500
001 - CONTROL NUMBER
control field 9781351231794
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220531132430.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 181112s2018 fluab ob 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781351231794
-- (e-book : PDF)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1053796340
040 ## - Cataloging Source
-- FlBoTFG
-- FlBoTFG
-- rda
041 1# -
-- eng
072 #7 -
-- TEC
-- 007000
-- bisacsh
072 #7 -
-- TEC
-- 008000
-- bisacsh
072 #7 -
-- THRB
-- bicscc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Arana-Daniel, Nancy,
Relator term author.
245 10 - TITLE STATEMENT
Title Neural Networks for Robotics :
Remainder of title An Engineering Perspective /
Statement of responsibility, etc. by Nancy Arana-Daniel, Alma Y. Alanis and Carlos Lopez-Franco.
250 ## - EDITION STATEMENT
Edition statement First edition.
264 #1 -
-- Boca Raton, FL :
-- CRC Press,
-- 2018.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (227 pages) :
Other physical details 176 illustrations, text file, PDF
336 ## -
-- text
-- rdacontent
337 ## -
-- computer
-- rdamedia
338 ## -
-- online resource
-- rdacarrier
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 00 -
-- Chapter 1 Recurrent High Order Neural Networks for rough terrain cost mapping --
-- 1.1 Introduction --
-- 1.2 Recurrent High Order Neural Networks, RHONN --
-- 1.3 Experimental results: identification of costs maps using RHONNs --
-- 1.4 Conclusions --
-- --Chapter 2 Geometric Neural Networks for object recognition --
-- 2.1 Object recognition and geometric representations of objects --
-- 2.2 Geometric algebra: An overview --
-- 2.3 Clifford SVM --
-- 2.4 Conformal neuron and hyper-conformal neurons --
-- 2.5 Conclusions --
-- --Chapter 3 Non-holonomic Mobile Robot Control using Recurrent High Order Neural Networks --
-- 3.1 Introduction --
-- 3.2 RHONN to Identify Uncertain Discrete-Time Nonlinear Systems --
-- 3.3 Neural Identification --
-- 3.4 Inverse Optimal Neural Control --
-- 3.5 IONC for Non-holonomic Mobile Robots --
-- 3.6 Conclusions --
-- --Chapter 4 Neural Networks for Autonomous Navigation on Nonholonomic Mobile Robots --
-- 4.1 Introduction --
-- 4.2 Simultaneous Localization and Mapping --
-- 4.3 Reinforcement Learning --
-- 4.4 Inverse Optimal Neural Controller --
-- 4.5 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 --
-- 5.5 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 --
-- 6.5 Visual Servo Control --
-- 6.6 Simulation results --
-- 6.7 Experimental Results --
-- 6.8 Conclusions
520 3# -
-- The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and aerial robots, and clustering, with real-time implementations. The reader will learn various methodologies that can be used to solve each stage on autonomous navigation for robots, from object recognition, clustering of obstacles, cost mapping of environments, path planning, and vision to low level control. These methodologies include real-life scenarios to implement a wide range of artificial neural network architectures.
530 ## -
-- Also available in print format.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element TECHNOLOGY & ENGINEERING / Electronics / General.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element cost mapping of environments.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element ground and aerial robots.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element intelligent control.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element pattern classification.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element robot navigation.
Source of heading or term bisacsh
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Robots
General subdivision Control systems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Neural networks (Computer science)
655 #0 -
-- Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Alanis, Alma Y.,
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Lopez-Franco, Carlos,
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Taylor and Francis.
776 08 -
-- Print version:
-- 9780815378686
856 40 -
-- https://www.taylorfrancis.com/books/9781351231794
-- Click here to view.

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