Despite the development of a now vast body of knowledge known as modern
control theory, and despite some spectacular applications of this theory to practical
situations, it is quite clear that some of the theory has yet to find application, and
many practical control problems have yet to find a theory that will successfully deal
with them. No one book, of course, can remedy the situation. The aim of this book
is to construct bridges that are still required for the student and practicing control
engineer between the familiar classical control results and those of modern control
theory.
Recent years have seen a rapid development of neural network control tech-
niques and their successful applications. Numerous simulation studies and
actual industrial implementations show that artificial neural network is a good
candidate for function approximation and control system design in solving the
control problems of complex nonlinear systems in the presence of different kinds
of uncertainties. Many control approaches/methods, reporting inventions and
control applications within the fields of adaptive control, neural control and
fuzzy systems, have been published in various books, journals and conference
proceedings.
This book will discuss the topic of Control Systems, which is an interdisciplinary engineering
topic. methods considered here will consist of both "Classical" control methods, and
"Modern" control methods. Also, discretely sampled systems (digital/computer systems) will
be considered in parallel with the more common analog methods. This book will not focus
on any single engineering discipline (electrical, mechanical, chemical, etc.), although readers
should have a solid foundation in the fundamentals of at least one discipline.
n recent years, there have been many books published on power system optimization.
Most of these books do not cover applications of artifi cial intelligence based methods.
Moreover, with the recent increase of artifi cial intelligence applications in various fi elds,
it is becoming a new trend in solving optimization problems in engineering in general
due to its advantages of being simple and effi cient in tackling complex problems. For this
reason, the application of artifi cial intelligence in power systems has attracted the interest
of many researchers around the world during the last two decades. This book is a result
of our effort to provide information on the latest applications of artifi cial intelligence
to optimization problems in power systems before and after deregulation.
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propa-
gation. Similarly, new models based on kernels have had significant impact on both
algorithms and applications.
The goal of this text is focus on a core subset of the natural language processing, unified
by the concepts of learning and search. A remarkable number of problems in natural
language processing can be solved by a compact set of methods:
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
The present era of research and development is all about interdisciplinary studies
attempting to better comprehend and model our understanding of this vast universe.
The fields of biology and computer science are no exception. This book discusses
some of the innumerable ways in which computational methods can be used to
facilitate research in biology and medicine—from storing enormous amounts of
biological data to solving complex biological problems and enhancing the treatment
of various diseases.
EN 300220-1V2.4.1 Electromagnetic compatibility and Radio spectrum Matters (ERM);Short Range Devices (SRD);Radio equipment to be used in the 25 MHz to 1 000 MHz frequency range with power levels ranging up to 500 mW; Part 1: Technical characteristics and test methods
This texts contemporary approach focuses on the concepts of linear control systems, rather than computational mechanics. Straightforward coverage includes an integrated treatment of both classical and modern control system methods. The text emphasizes design with discussions of problem formulation, design criteria, physical constraints, several design methods, and implementation of compensators.Discussions of topics not found in other texts--such as pole placement, model matching and robust tracking--add to the texts cutting-edge presentation. Students will appreciate the applications and discussions of practical aspects, including the leading problem in developing block diagrams, noise, disturbances, and plant perturbations. State feedback and state estimators are designed using state variable equations and transfer functions, offering a comparison of the two approaches. The incorporation of MATLAB throughout the text helps students to avoid time-consuming computation and concentrate on control system design and analysis
