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Significant progress has been made on nonlinear control systems in
the past two decades. However, many of the existing nonlinear
control methods cannot be readily used to cope with communication
and networking issues without nontrivial modifications. For
example, small quantization errors may cause the performance of a
"well-designed" nonlinear control system to deteriorate. Motivated
by the need for new tools to solve complex problems resulting from
smart power grids, biological processes, distributed computing
networks, transportation networks, robotic systems, and other
cutting-edge control applications, Nonlinear Control of Dynamic
Networks tackles newly arising theoretical and real-world
challenges for stability analysis and control design, including
nonlinearity, dimensionality, uncertainty, and information
constraints as well as behaviors stemming from quantization,
data-sampling, and impulses. Delivering a systematic review of the
nonlinear small-gain theorems, the text: Supplies novel
cyclic-small-gain theorems for large-scale nonlinear dynamic
networks Offers a cyclic-small-gain framework for nonlinear control
with static or dynamic quantization Contains a combination of
cyclic-small-gain and set-valued map designs for robust control of
nonlinear uncertain systems subject to sensor noise Presents a
cyclic-small-gain result in directed graphs and distributed control
of nonlinear multi-agent systems with fixed or dynamically changing
topology Based on the authors' recent research, Nonlinear Control
of Dynamic Networks provides a unified framework for robust,
quantized, and distributed control under information constraints.
Suggesting avenues for further exploration, the book encourages
readers to take into consideration more communication and
networking issues in control designs to better handle the arising
challenges.
Significant progress has been made on nonlinear control systems in
the past two decades. However, many of the existing nonlinear
control methods cannot be readily used to cope with communication
and networking issues without nontrivial modifications. For
example, small quantization errors may cause the performance of a
"well-designed" nonlinear control system to deteriorate. Motivated
by the need for new tools to solve complex problems resulting from
smart power grids, biological processes, distributed computing
networks, transportation networks, robotic systems, and other
cutting-edge control applications, Nonlinear Control of Dynamic
Networks tackles newly arising theoretical and real-world
challenges for stability analysis and control design, including
nonlinearity, dimensionality, uncertainty, and information
constraints as well as behaviors stemming from quantization,
data-sampling, and impulses. Delivering a systematic review of the
nonlinear small-gain theorems, the text: Supplies novel
cyclic-small-gain theorems for large-scale nonlinear dynamic
networks Offers a cyclic-small-gain framework for nonlinear control
with static or dynamic quantization Contains a combination of
cyclic-small-gain and set-valued map designs for robust control of
nonlinear uncertain systems subject to sensor noise Presents a
cyclic-small-gain result in directed graphs and distributed control
of nonlinear multi-agent systems with fixed or dynamically changing
topology Based on the authors' recent research, Nonlinear Control
of Dynamic Networks provides a unified framework for robust,
quantized, and distributed control under information constraints.
Suggesting avenues for further exploration, the book encourages
readers to take into consideration more communication and
networking issues in control designs to better handle the arising
challenges.
Deterministic Learning Theory for Identification, Recognition, and
Control presents a unified conceptual framework for knowledge
acquisition, representation, and knowledge utilization in uncertain
dynamic environments. It provides systematic design approaches for
identification, recognition, and control of linear uncertain
systems. Unlike many books currently available that focus on
statistical principles, this book stresses learning through
closed-loop neural control, effective representation and
recognition of temporal patterns in a deterministic way. A
Deterministic View of Learning in Dynamic Environments The authors
begin with an introduction to the concepts of deterministic
learning theory, followed by a discussion of the persistent
excitation property of RBF networks. They describe the elements of
deterministic learning, and address dynamical pattern recognition
and pattern-based control processes. The results are applicable to
areas such as detection and isolation of oscillation faults,
ECG/EEG pattern recognition, robot learning and control, and
security analysis and control of power systems. A New Model of
Information Processing This book elucidates a learning theory which
is developed using concepts and tools from the discipline of
systems and control. Fundamental knowledge about system dynamics is
obtained from dynamical processes, and is then utilized to achieve
rapid recognition of dynamical patterns and pattern-based
closed-loop control via the so-called internal and dynamical
matching of system dynamics. This actually represents a new model
of information processing, i.e. a model of dynamical parallel
distributed processing (DPDP).
This is a reproduction of a book published before 1923. This book
may have occasional imperfections such as missing or blurred pages,
poor pictures, errant marks, etc. that were either part of the
original artifact, or were introduced by the scanning process. We
believe this work is culturally important, and despite the
imperfections, have elected to bring it back into print as part of
our continuing commitment to the preservation of printed works
worldwide. We appreciate your understanding of the imperfections in
the preservation process, and hope you enjoy this valuable book.
++++ The below data was compiled from various identification fields
in the bibliographic record of this title. This data is provided as
an additional tool in helping to ensure edition identification:
++++ The Elements Of Logic: A Text-book William Stanley Jevons,
David J. Hill Philosophy; Logic; Logic; Philosophy / Logic
This scarce antiquarian book is a selection from Kessinger
PublishingA AcentsAcentsa A-Acentsa Acentss Legacy Reprint Series.
Due to its age, it may contain imperfections such as marks,
notations, marginalia and flawed pages. Because we believe this
work is culturally important, we have made it available as part of
our commitment to protecting, preserving, and promoting the world's
literature. Kessinger Publishing is the place to find hundreds of
thousands of rare and hard-to-find books with something of intere
This book is a facsimile reprint and may contain imperfections such
as marks, notations, marginalia and flawed pages.
Rotor angle stability is a topic of fundamental importance in
electric power systems. Traditionally, rotor angle stability
analysis is oriented to node dynamics, especially the impact of
generator modeling and parameters. On the other hand, the power
network structural information is simply treated as some
coefficients in the system dynamical models, which have been paid
less attention. This monograph surveys the network-based theories
of rotor angle stability that elaborate the role of power network
structure, including the results developed in early years as well
as in recent years that are facilitated by the new progress on
graph theory. It focuses on the connections between power network
structures and system dynamic behaviors, and those graph theoretic
tools tailored for power system analysis.This publication provides
new insights into some important problems in rotor angle stability
that have not been well addressed by the traditional node-based
approaches. Network-Based Analysis of Rotor Angle Stability of
Power Systems is a must-read for all students and researchers
working on the cutting edge of electric power systems.
A perfect introduction to introductory human anatomy and
physiology, Essentials of Anatomy & Physiology Laboratory
Manual offers a unique approach that incorporates crime scenes,
superheroes and more. While traditional lab manuals simply offer
core concepts on A&P topics, this one-of-a-kind resource
presents material from easily understood comparisons to help you
learn about A&P from a real-world point of view. Plus, hands-on
activities experiments help link what you're learning today with
how it may be used in your professional life. Labeling exercises
help you memorize the small details of complicated body parts and
processes. Practical experiments that center on your own
physiological processes and knowledge of the world in general help
you make connections between the text, lab, and the world around
you. Numerous full-color illustrations and photomicrographs help
you visualize difficult concepts and reinforce development of
spatial perspective.
Deterministic Learning Theory for Identification, Recognition,
and Control presents a unified conceptual framework for knowledge
acquisition, representation, and knowledge utilization in uncertain
dynamic environments. It provides systematic design approaches for
identification, recognition, and control of linear uncertain
systems. Unlike many books currently available that focus on
statistical principles, this book stresses learning through
closed-loop neural control, effective representation and
recognition of temporal patterns in a deterministic way.
A Deterministic View of Learning in Dynamic Environments
The authors begin with an introduction to the concepts of
deterministic learning theory, followed by a discussion of the
persistent excitation property of RBF networks. They describe the
elements of deterministic learning, and address dynamical pattern
recognition and pattern-based control processes. The results are
applicable to areas such as detection and isolation of oscillation
faults, ECG/EEG pattern recognition, robot learning and control,
and security analysis and control of power systems.
A New Model of Information Processing
This book elucidates a learning theory which is developed using
concepts and tools from the discipline of systems and control.
Fundamental knowledge about system dynamics is obtained from
dynamical processes, and is then utilized to achieve rapid
recognition of dynamical patterns and pattern-based closed-loop
control via the so-called internal and dynamical matching of system
dynamics. This actually represents a new model of information
processing, i.e. a model of dynamical parallel distributed
processing (DPDP).
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