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Optimization, Learning, and Control for Interdependent Complex Networks

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Cover of 'Optimization, Learning, and Control for Interdependent Complex Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Panorama of Optimization, Control, and Learning Algorithms for Interdependent SWEET (Societal, Water, Energy, Economic, and Transportation) Networks
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    Chapter 2 Promises of Fully Distributed Optimization for IoT-Based Smart City Infrastructures
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    Chapter 3 Evolutionary Computation, Optimization, and Learning Algorithms for Data Science
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    Chapter 4 Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics
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    Chapter 5 Feature Selection in High-Dimensional Data
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    Chapter 6 An Introduction to Advanced Machine Learning: Meta-Learning Algorithms, Applications, and Promises
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    Chapter 7 Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications
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    Chapter 8 Bi-level Adversary-Operator Cyberattack Framework and Algorithms for Transmission Networks in Smart Grids
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    Chapter 9 Toward Operational Resilience of Smart Energy Networks in Complex Infrastructures
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    Chapter 10 Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms
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    Chapter 11 An Optimal Approach for Load-Frequency Control of Islanded Microgrids Based on Nonlinear Model
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    Chapter 12 Photovoltaic Design for Smart Cities and Demand Forecasting Using a Truncated Conjugate Gradient Algorithm
Attention for Chapter 10: Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms
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Citations

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Chapter title
Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms
Chapter number 10
Book title
Optimization, Learning, and Control for Interdependent Complex Networks
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-34094-0_10
Book ISBNs
978-3-03-034093-3, 978-3-03-034094-0
Authors

Arman Sargolzaei, Alireza Abbaspour, Carl D. Crane

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Student > Master 7 19%
Student > Doctoral Student 3 8%
Other 2 6%
Lecturer > Senior Lecturer 2 6%
Other 3 8%
Unknown 11 31%
Readers by discipline Count As %
Engineering 16 44%
Computer Science 6 17%
Agricultural and Biological Sciences 2 6%
Social Sciences 1 3%
Unknown 11 31%