|
Showing 1 - 25 of
217 matches in All Departments
This book explores sacrifice as a narrative theme and a stylistic
strategy in works by May Sinclair, Mary Butts and H. D. It argues
that the modernist experiment with pronoun use informs the
treatment of acts of sacrifice in the texts, understood both as
acts of self-renunciation and as ritual performance. It also
suggests that sacrifice, if the conditions are right, can serve as
the structure upon which a cohesive community might be built. The
book offers in-depth analyses of the three authors and their works,
deftly dissecting the modernist narrative experiment to show that
it was by no means limited — it was a means by which to approach
a wide range of stories and materials.
We describe in this book, recent developments on fuzzy logic,
neural networks and optimization algorithms, as well as their
hybrid combinations, and their application in areas such as,
intelligent control and robotics, pattern recognition, medical
diagnosis, time series prediction and optimization of complex
problems. The book contains a collection of papers focused on
hybrid intelligent systems based on soft computing. There are some
papers with the main theme of type-1 and type-2 fuzzy logic, which
basically consists of papers that propose new concepts and
algorithms based on type-1 and type-2 fuzzy logic and their
applications. There also some papers that presents theory and
practice of meta-heuristics in different areas of application.
Another group of papers describe diverse applications of fuzzy
logic, neural networks and hybrid intelligent systems in medical
applications. There are also some papers that present theory and
practice of neural networks in different areas of application. In
addition, there are papers that present theory and practice of
optimization and evolutionary algorithms in different areas of
application. Finally, there are some papers describing applications
of fuzzy logic, neural networks and meta-heuristics in pattern
recognition problems.
Ancient epistolary fiction is a still largely under-explored field
of research, at the intersection of studies on epistolography and
on pseudepigraphy. The present volume sketches out a broad panorama
of ancient fiction in letters. It covers a large period of time up
to late Antiquity, with a main focus on letters from the imperial
era. Epistolary fiction is examined as a mainly Greek phenomenon
(there are few Latin equivalents) that was characteristic of both
pagan and Christian literature. The material investigated falls
within two categories: fictional letter collections from well-known
authors of the Second Sophistic and their successors (Lucian,
Alciphron, Philostratus, Aristaenetus); letters attributed to
famous historical or legendary characters (pseudonymous letters).
Focusing on the specific features of epistolary fiction, the book
aims to analyse its forms, its functions as well as its effects. It
gathers a series of 11 state-of-the art essays, all tackling the
same important issues: the manuscript and printed tradition, the
form of epistolary fictions and the universe they build, the
arrangement of the letters and their overall structure, the
relation between the author and his external readers.
This book describes the latest advances in fuzzy logic, neural
networks, and optimization algorithms, as well as their hybrid
intelligent combinations, and their applications in the areas such
as intelligent control, robotics, pattern recognition, medical
diagnosis, time series prediction, and optimization. The topic is
highly relevant as most current intelligent systems and devices use
some form of intelligent feature to enhance their performance. The
book also presents new and advanced models and algorithms of type-2
fuzzy logic and intuitionistic fuzzy systems, which are of great
interest to researchers in these areas. Further, it proposes novel,
nature-inspired optimization algorithms and innovative neural
models. Featuring contributions on theoretical aspects as well as
applications, the book appeals to a wide audience.
This book describes the latest advances in fuzzy logic, neural
networks and optimization algorithms, as well as their hybrid
combinations, and their applications in areas such as: intelligent
control and robotics, pattern recognition, medical diagnosis, time
series prediction, and optimization of complex problems. The book
is divided into five main parts. The first part proposes new
concepts and algorithms based on type-1 and type-2 fuzzy logic and
their applications; the second explores new concepts and algorithms
in neural networks and fuzzy logic applied to recognition. The
third part examines the theory and practice of meta-heuristics in
various areas of application, while the fourth highlights diverse
applications of fuzzy logic, neural networks and hybrid intelligent
systems in medical contexts. Finally, the fifth part focuses on
applications of fuzzy logic, neural networks and meta-heuristics to
robotics problems.
This book describes recent advances on hybrid intelligent
systems using soft computing techniques for diverse areas of
application, such as intelligent control and robotics, pattern
recognition, time series prediction and optimization complex
problems. Soft Computing (SC) consists of several intelligent
computing paradigms, including fuzzy logic, neural networks and
bio-inspired optimization algorithms, which can be used to produce
powerful hybrid intelligent systems. The book is organized in five
main parts, which contain a group of papers around a similar
subject. The first part consists of papers with the main theme of
type-2 fuzzy logic, which basically consists of papers that propose
new models and applications for type-2 fuzzy systems. The second
part contains papers with the main theme of bio-inspired
optimization algorithms, which are basically papers using
nature-inspired techniques to achieve optimization of complex
optimization problems in diverse areas of application. The third
part contains papers that deal with new models and applications of
neural networks in real world problems. The fourth part contains
papers with the theme of intelligent optimization methods, which
basically consider the proposal of new methods of optimization to
solve complex real world optimization problems. The fifth part
contains papers with the theme of evolutionary methods and
intelligent computing, which are papers considering soft computing
methods for applications related to diverse areas, such as natural
language processing, recommending systems and optimization.
This book describes recent advances on fuzzy logic, neural networks
and optimization algorithms, as well as their hybrid combinations,
and their application in areas such as intelligent control and
robotics, pattern recognition, medical diagnosis, time series
prediction and optimization of complex problems. The book contains
a collection of papers focused on hybrid intelligent systems based
on soft computing. There are some papers with the main theme of
type-1 and type-2 fuzzy logic, which basically consists of papers
that propose new concepts and algorithms based on type-1 and type-2
fuzzy logic and their applications. There are also some papers that
present theory and practice of meta-heuristics in different areas
of application. Another group of papers describes diverse
applications of fuzzy logic, neural networks and hybrid intelligent
systems in medical applications. There are also some papers that
present theory and practice of neural networks in different areas
of application. In addition, there are papers that present theory
and practice of optimization and evolutionary algorithms in
different areas of application. Finally, there are some papers
describing applications of fuzzy logic, neural networks and
meta-heuristics in pattern recognition problems.
This book highlights recent advances in the design of hybrid
intelligent systems based on nature-inspired optimization and their
application in areas such as intelligent control and robotics,
pattern recognition, time series prediction, and optimization of
complex problems. The book is divided into seven main parts, the
first of which addresses theoretical aspects of and new concepts
and algorithms based on type-2 and intuitionistic fuzzy logic
systems. The second part focuses on neural network theory, and
explores the applications of neural networks in diverse areas, such
as time series prediction and pattern recognition. The book's third
part presents enhancements to meta-heuristics based on fuzzy logic
techniques and describes new nature-inspired optimization
algorithms that employ fuzzy dynamic adaptation of parameters,
while the fourth part presents diverse applications of
nature-inspired optimization algorithms. In turn, the fifth part
investigates applications of fuzzy logic in diverse areas, such as
time series prediction and pattern recognition. The sixth part
examines new optimization algorithms and their applications.
Lastly, the seventh part is dedicated to the design and application
of different hybrid intelligent systems.
This is an excellent book that provides a much needed overview of
theoretical frameworks that help us understand the behavior of
family firms. This book will be invaluable as a resource to
doctoral students and researchers who are trying to analyze
decision making in family firms and the factors that shape
management processes in these organizations. The book is very
comprehensive in its coverage, addressing most major theoretical
frameworks that have recently been applied to examine phenomena
unique to family firms. These include, among others, organizational
ecology, behavioral theory, agency theory, behavioral agency
theory, and social identity theory. This book is a must read for
family business scholars.' - Luis Gomez-Mejia, University of Notre
Dame, US'A common criticism of family business as a field of study
is the lack of well-developed, theoretically grounded research.
This important book edited by Nordqvist, Melin, Waldkirch and
Kumeto promises to change this widely held view. The book brings
into focus important theoretical perspectives that have rich
implications for research on family firms. Well established and
emerging perspectives are covered in the book, highlighting
different debates that are likely to shape future scholarship in
the field. Chapters are easy to read and concepts as well as
theories are clearly explained and their usefulness is made
evident. I compliment the editors on a job well done in recognizing
the challenges of theorizing about family business and mapping out
the territory of tomorrow's research.' - Shaker A. Zahra,
University of Minnesota, US Family business has become an
increasingly studied field over the last decade and forms one of
the fastest growing research areas today. The uniqueness of family
business is the interaction between two systems: the family and the
firm, leading to specific characteristics that we rarely see in
other types of businesses. In order to understand the unique
interaction between these two systems, researchers have adopted a
diverse range of theories drawn from different fields. The
contributors to this volume provide a thorough discussion of 13
theoretical perspectives that have been used in family business
research. Each chapter introduces a theory, demonstrates its
previous application in family business research and offers
compelling ideas for future research that could contribute to both
the family business field and the original theory behind it. This
book aims to spark new insights for researchers and PhD students in
the field of family business, and is also a good introduction for
researchers who are new to the field. Policymakers and
practitioners will find this an enlightening resource.
Contributors: R. Adiguna, N. Akhter, G. Criaco, P. Hansson, A.
Kallmuenzer, A. Kuiken, G. Kumeto, A. Mazzelli, L. Melin, H.
Nilson, M. Nordqvist, E. Rondi, K. Staffansson Pauli, M. Waldkirch,
A. Wielsma
This book describes new methods for building intelligent systems
using type-2 fuzzy logic and soft computing (SC) techniques. The
authors extend the use of fuzzy logic to a higher order, which is
called type-2 fuzzy logic. Combining type-2 fuzzy logic with
traditional SC techniques, we can build powerful hybrid intelligent
systems that can use the advantages that each technique offers.
This book is intended to be a major reference tool and can be used
as a textbook.
This book presents recent advances on the design of intelligent
systems based on fuzzy logic, neural networks and nature-inspired
optimization and their application in areas such as, intelligent
control and robotics, pattern recognition, time series prediction
and optimization of complex problems. The book is organized in
eight main parts, which contain a group of papers around a similar
subject. The first part consists of papers with the main theme of
theoretical aspects of fuzzy logic, which basically consists of
papers that propose new concepts and algorithms based on fuzzy
systems. The second part contains papers with the main theme of
neural networks theory, which are basically papers dealing with new
concepts and algorithms in neural networks. The third part contains
papers describing applications of neural networks in diverse areas,
such as time series prediction and pattern recognition. The fourth
part contains papers describing new nature-inspired optimization
algorithms. The fifth part presents diverse applications of
nature-inspired optimization algorithms. The sixth part contains
papers describing new optimization algorithms. The seventh part
contains papers describing applications of fuzzy logic in diverse
areas, such as time series prediction and pattern recognition.
Finally, the eighth part contains papers that present enhancements
to meta-heuristics based on fuzzy logic techniques.
This book describes hybrid intelligent systems using type-2 fuzzy
logic and modular neural networks for pattern recognition
applications. Hybrid intelligent systems combine several
intelligent computing paradigms, including fuzzy logic, neural
networks, and bio-inspired optimization algorithms, which can be
used to produce powerful pattern recognition systems. Type-2 fuzzy
logic is an extension of traditional type-1 fuzzy logic that
enables managing higher levels of uncertainty in complex real world
problems, which are of particular importance in the area of pattern
recognition. The book is organized in three main parts, each
containing a group of chapters built around a similar subject. The
first part consists of chapters with the main theme of theory and
design algorithms, which are basically chapters that propose new
models and concepts, which are the basis for achieving intelligent
pattern recognition. The second part contains chapters with the
main theme of using type-2 fuzzy models and modular neural networks
with the aim of designing intelligent systems for complex pattern
recognition problems, including iris, ear, face and voice
recognition. The third part contains chapters with the theme of
evolutionary optimization of type-2 fuzzy systems and modular
neural networks in the area of intelligent pattern recognition,
which includes the application of genetic algorithms for obtaining
optimal type-2 fuzzy integration systems and ideal neural network
architectures for solving problems in this area.
|
|