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Deterministic Learning Theory for Identification, Recognition, and Control

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge;

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Deterministic Learning Theory for Identification, Recognition, and Control

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge;

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General Pontryagin-Type Stochastic Maximum Principle and Backward Stochastic Evolution Equations in Infinite Dimensions

in modern control theory. The corresponding theory is by now well-developed in the deterministic infinite dimensional setting and for the stochastic;

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Advances in H Control Theory

mathematical fundamentals: -algebra and the input-output method for retarded systems. Advances in H Control Theory is written for engineers engaged;

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Differential Games and Control Theory Iii

This book is based on the Third Kingston Conference on Differential Games and Control Theory held at the University of Rhode Island June 5;

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Differential Games and Control Theory Iii

This book is based on the Third Kingston Conference on Differential Games and Control Theory held at the University of Rhode Island June 5;

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Chaotic Transitions in Deterministic and Stochastic Dynamical Systems

and its deterministic or stochastic excitation, and is a powerful modeling and identification tool. The book is designed primarily for readers;

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Signals and Control Systems

analysis of their behavior. Finally we discuss the estimation, identification and synthesis of control laws for the purpose of stabilization and;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Mathematical Control Theory

, realization theory for nonlinear systems, impulsive control and positive systems, the control of rigid bodies, the stabilization of infinite;

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Low Rank Approximation

Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control;

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Optimal Design of Control Systems: Stochastic and Deterministic Problems (Pure and Applied Mathematics

"Covers design methods for optimal (or quasioptimal) control algorithms in the form of synthesis for deterministic and stochastic dynamical;

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Brain and Nature-Inspired Learning, Computation and Recognition

Brain and Nature-Inspired Learning, Computation and Recognition presents a systematic analysis of neural networks, natural computing;

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Controlled Markov Processes and Viscosity Solutions

This book is intended as an introduction to optimal stochastic control for continuous time Markov processes and to the theory of viscosity;

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Self-Learning Speaker Identification

characteristics. This work develops an integrated approach for speech and speaker recognition in order to gain space for self-learning opportunities;

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Deep Learning for Biometrics

-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics;

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Mathematical Control Theory for Stochastic Partial Differential Equations

, partial differential equations, and control theory for deterministic systems is the only prerequisite for reading this book.;

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Stochastic Control of Partially Observable Systems

in fact of this type, and deterministic control as well as stochastic control with full observation can only be approximations to the real world;

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Advances in Computational Intelligence

; artificial neural network for biomedical image processing; machine learning in vision and robotics; system identification, process control, and;

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Advances in Computational Intelligence

; artificial neural network for biomedical image processing; machine learning in vision and robotics; system identification, process control, and;

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An Elementary Introduction to Statistical Learning Theory

is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory;

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Neural Approximations for Optimal Control and Decision

errors of approximate solutions; * solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect;

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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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Design of Linear Multivariable Feedback Control Systems

feedback control systems that insure stable system errors and stable controller outputs for persistent deterministic reference inputs that are;

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Design of Linear Multivariable Feedback Control Systems

feedback control systems that insure stable system errors and stable controller outputs for persistent deterministic reference inputs that are;

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Adaptive Control Processes - A Guided Tour

The aim of this work is to present a unified approach to the modern field of control theory and to provide a technique for making problems;

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