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Machine Learning and Systems Engineering

Kataloginformation Katalogdatensatz500174836 .

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Feldname	Details
Vorliegende Sprache	eng
ISBN	978-90-481-9418-6
Name	Ao, Sio-Iong
Name	Rieger, Burghard
Name ANZEIGE DER KETTE	Rieger, Burghard
Name	Amouzegar, Mahyar A.
T I T E L	Machine Learning and Systems Engineering
Verlagsort	Dordrecht
Verlag	Springer Science+Business Media B.V
Erscheinungsjahr	2011
Erscheinungsjahr	2011
Umfang	Online-Ressource (XXII, 612p, digital)
Reihe	Lecture Notes in Electrical Engineering ; 68
Notiz / Fußnoten	Description based upon print version of record
Weiterer Inhalt	Machine Learning and Systems Engineering; Preface; Contents; Chapter 1: Multimodal Human Spacecraft Interaction in Remote Environments; Chapter 2: A Framework for Collaborative Aspects of Intelligent Service Robot; Chapter 3: Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving; Chapter 4: Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem; Chapter 5: A Development of Data-Logger for Indoor Environment; Chapter 6: Multiobjective Evolutionary Optimization and Machine Learning: Application to Renewable Energy Predictions. Chapter 7: Hybriding Intelligent Host-Based and Network-Based Stepping Stone DetectionsChapter 8: Open Source Software Use in City Government; Chapter 9: Pheromone-Balance Driven Ant Colony Optimization with Greedy Mechanism; Chapter 10: Study of Pitchfork Bifurcation in Discrete Hopfield Neural Network; Chapter 11: Grammatical Evolution and STE Criterion; Chapter 12: Data Quality in ANFIS Based Soft Sensors; Chapter 13: The Meccano Method for Automatic Volume Parametrization of Solids; Chapter 14: A Buck Converter Model for Multi-Domain Simulations. Chapter 15: The Computer Simulation of Shaping in Rotating Electrical Discharge MachiningChapter 16: Parameter Identification of a Nonlinear Two Mass System Using Prior Knowledge; Chapter 17: Adaptive and Neural Learning for Biped Robot Actuator Control; Chapter 18: Modeling, Simulation, and Analysis for Battery Electric Vehicles; Chapter 19: Modeling Confined Jets with Particles and Swril*; Chapter 20: Robust Tracking and Control of MIMO Processes with Input Saturation and Unknown Disturbance. Chapter 21: Analysis of Priority Rule-Based Scheduling in Dual-Resource-Constrained Shop-Floor ScenariosChapter 22: A Hybrid Framework for Servo-Actuated Systems Fault Diagnosis; Chapter 23: Multigrid Finite Volume Method for FGF-2 Transport and Binding; Chapter 24: Integrated Mining Fuzzy Association Rules For Mineral Processing State Identification; Chapter 25: A Combined Cycle Power Plant Simulator: A Powerful, Competitive, and Useful Tool for Operator's Training; Chapter 26: Texture Features Extraction in Mammograms Using Non-Shannon Entropies. Chapter 27: A Wideband DOA Estimation Method Based on Arbitrary Group DelayChapter 28: Spatial Speaker Spatial Positioning of Synthesized Speech in Java; Chapter 29: Commercial Break Detection and Content Based Cideo Retrieval; Chapter 30: ClusterDAM: Clustering Mechanism for Delivery of Adaptive Multimedia Content in Two-Hop Wireless Networks; Chapter 31: Ranking Intervals in Complex Stochastic Boolean Systems Using Intrinsic Ordering; Chapter 32: Predicting Memory Phases; Chapter 33: Information Security Enhancement to Public-Key Cryptosystem Through Magic Squares. Chapter 34: Resource Allocation for Grid Applications: An Economy Model
Titelhinweis	Buchausg. u.d.T.ISBN: 978-90-481-9418-6
ISBN	ISBN 978-90-481-9419-3
Klassifikation	UYQ
	COM004000
	*68T05
	68-06
	68Txx
	92-08
	006.3
	006.31
	Q342
Kurzbeschreibung	A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
SWB-Titel-Idn	333467167
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-90-481-9419-3
Internetseite / Link	Volltext
Siehe auch	Volltext
Siehe auch	Cover
Siehe auch	Inhaltsverzeichnis
Siehe auch	Einführung/Vorwort
Siehe auch	Inhaltstext

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