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Optimization, Learning, and Control for Interdependent Complex Networks

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role;

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Sustainable Interdependent Networks

of sustainable interdependent networks. The fourth and last part of this volume addresses the promises of control and management techniques for the;

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Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments

This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi;

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Network Models for Control and Processing

This book provides a powerful tool for collecting and correlating related bodies of research in modelling control and processing;

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Deep Neural Networks and Applications

. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research;

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Neural Approximations for Optimal Control and Decision

Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional;

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Cooperative and Graph Signal Processing

, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the;

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Distributed Optimization Advances in Theories Methods and Applications

of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.;

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Distributed Optimization Advances in Theories Methods and Applications

of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.;

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Applied Optimization Methods for Wireless Networks

case study taken from state-of-the-art research. You will learn various tips and step-by-step instructions for developing optimization models;

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Federated Learning for Wireless Networks

computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management;

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Impulsive Synchronization of Complex Dynamical Networks

influence of impulsive control in the design and optimization of complex networks. The primary audience for the book would be the scholars and;

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Network Games, Control, and Optimization

This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP;

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Optimization Techniques for Solving Complex Problems

.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world;

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Optimization and Control Methods in Industrial Engineering and Construction

This book presents recent advances in optimization and control methods with applications to industrial engineering and construction;

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Deep Learning for Data Analytics

, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning;

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Intelligent Optimization and Control of Complex Metallurgical Processes

This book discusses the intelligent optimization and control of complex metallurgical processes, including intelligent optimization and;

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Intelligent Optimization and Control of Complex Metallurgical Processes

This book discusses the intelligent optimization and control of complex metallurgical processes, including intelligent optimization and;

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Nonlinear Pinning Control of Complex Dynamical Networks

This book presents two nonlinear control strategies for complex dynamical networks. First, sliding-mode control is used, and then the;

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Optimal Control And Forecasting Of Complex Dynamical Systems

This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance;

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Machine Learning for Networking

learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new;

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Hardware Architectures for Deep Learning

, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for;

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Recent Advances on Hybrid Approaches for Designing Intelligent Systems

intelligent control and robotics, pattern recognition, time series prediction and optimization complex problems. Soft Computing (SC) consists;

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Advances in Swarm Intelligence

algorithms; multi-objective optimization; large-scale global optimization; biometrics; scheduling and planning; machine learning methods; clustering;

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Advances in Swarm Intelligence

algorithms; multi-objective optimization; large-scale global optimization; biometrics; scheduling and planning; machine learning methods; clustering;

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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author;

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