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Iterative Algorithms For Multilayer Optimizing Control

The book presents basic structures, concepts and algorithms in the area of multilayer optimizing control of industrial systems, as well as;

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Iterative Learning Stabilization and Fault Tolerant Control for Batch Processes

topics in the field of control system engineering. It introduces iterative learning control for linear/nonlinear single/multi-phase batch;

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Approximate Iterative Algorithms

functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error;

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Approximate Iterative Algorithms

functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error;

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Iterative Learning Control Algorithms and Experimental Benchmarking

in healthcare systems/ rehabilitation robotics. The book is essential reading for researchers and graduate students in iterative learning control;

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Parallel Iterative Algorithms

parallel synchronous and asynchronous iterative algorithms for numerical computation, with an emphasis on the multisplitting approach. The final;

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Parallel Iterative Algorithms

parallel synchronous and asynchronous iterative algorithms for numerical computation, with an emphasis on the multisplitting approach. The final;

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Iterative Receiver Design

provides a general framework for systematically developing iterative algorithms for digital receivers, and highlights connections between important;

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Applied Iterative Methods

applicable to partitioning the linear system into a “red/black” block form, adaptive computational algorithms for the successive overrelaxation (SOR;

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Learning Decision Sequences For Repetitive Processes-Selected Algorithms

This book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified;

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Iterative Computer Algorithms With Applications In Engineering

Iterative Computer Algorithms with Applications in Engineering describes in-depth the five main iterative algorithms for solving hard;

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Applied Iterative Methods

Applied Iterative Methods is a self-contained treatise suitable as both a reference and a graduate-l;...

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Applied Iterative Methods

Applied Iterative Methods is a self-contained treatise suitable as both a reference and a graduate-l;...

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Iterative Optimization in Inverse Problems

Iterative Optimization in Inverse Problems brings together a number of important iterative algorithms for medical imaging, optimization;

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A Brief Introduction to Continuous Evolutionary Optimization

problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization;

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Active Sound and Vibration Control

for the control of sound and vibration, and this book covers the latest theoretical, algorithmic and practical applications including: noise;

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Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving;

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Iterative Learning Control for Multi-agent Systems Coordination

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances;

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An Introduction to Iterative Toeplitz Solvers

, minimal realization problems in control theory, system identification problems in signal processing, and image restoration problems in image;

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From Plant Data to Process Control

control, two key components of process engineering, essential for optimizing production systems. It examines both the theoretical advances;

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Optimization in Solving Elliptic Problems

- the optimization of numerical algorithms for solving elliptic problems. It presents detailed discussions of how asymptotically optimal;

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Principal Component Analysis Networks and Algorithms

This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA;

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Data-Driven Model-Free Controllers

in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the;

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Two-Degree-of-Freedom Control Systems

parameterization (YP). This concept is introduced and extended for TDOF control loops. The Keviczky-Banyasz parameterization (KP) method developed for;

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Regularization Algorithms for Ill-Posed Problems

operator equations, both linear and nonlinear. These approximation schemes form a basis for implementable numerical algorithms for the stable;

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Network Games, Control and Optimization

This book constitutes the conference proceedings of the 10th International Conference on Network Games, Control and Optimization, NETGCOOP;

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Intelligent Algorithms for Analysis and Control of Dynamical Systems

control of dynamical systems. Both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems are explored for analysis and;

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