Cantitate/Preț
Produs

Numerical and Evolutionary Optimization – NEO 2017: Studies in Computational Intelligence, cartea 785

Editat de Leonardo Trujillo, Oliver Schütze, Yazmin Maldonado, Paul Valle
en Limba Engleză Paperback – 22 dec 2018
This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 127308 lei  43-57 zile
  Springer International Publishing – 22 dec 2018 127308 lei  43-57 zile
Hardback (1) 127953 lei  43-57 zile
  Springer International Publishing – 13 iul 2018 127953 lei  43-57 zile

Din seria Studies in Computational Intelligence

Preț: 127308 lei

Preț vechi: 159136 lei
-20% Nou

Puncte Express: 1910

Preț estimativ în valută:
24363 25281$ 20363£

Carte tipărită la comandă

Livrare economică 17-31 martie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030071448
ISBN-10: 3030071448
Pagini: 312
Ilustrații: XIV, 312 p. 137 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.46 kg
Ediția:Softcover reprint of the original 1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Deterministic Parameter Control in Differential Evolution with Combined Variants for Constrained Search Spaces.- A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems.- Evaluating Memetic Building Spatial Design Optimisation Using Hypervolume Indicator Gradient Ascent.- Fitting Multiple Ellipses with PEARL and a Multi-objective Genetic Algorithm.- Analyzing Evolutionary Art Audience Interaction by Means of a Kinect Based Non-Intrusive Method.- Applying Control Theory to Optimize the Inventory Holding Costs in Supply Chains.- On the Selection of Tuning Parameters in Predictive Controllers Based on NSGA-II.- IDA-PBC Controller Tuning Using Steepest Descent.- Self-Tuning for a SISO-Type Fuzzy Control Based on the Relay Feedback Approach.- Optimal Design Of Sliding Mode Control Combined with Positive Position Feedback.- Biot’s Parameters Estimation In Ultrasound Propagation Through Cancellous Bone.- Optimal Sizing of Low-DropOut Voltage Regulators by NSGA-II and PVT Analysis.- Genetic Optimization of Fuzzy Systems for the Classification of Treated Water Quality.- Stabilization Based on Fuzzy System for Structures Affected by External Disturbances.- Comparison of Two Methods for I/Q Imbalance Compensation Applied in RF Power Amplifiers.- An Application of Data Envelopment Analysis to the Performance Assessment of Online Social Networks Usage in Mazatlan Hotel Organizations. 

Textul de pe ultima copertă

This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.

Caracteristici

Presents recent numerical and evolutionary optimization research Includes the outcomes of the Numerical and Evolutionary Optimization (NEO 2017) workshop held in Tijuana, Mexico Written by experts in the field