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Understanding
Neural Networks
John Iovine
Synopsis
A guide to the nuts-and-bolts of neural networks. John Iovine
explains the differences between traditional rule-based
(symbolic) computer processors and the mind-boggling possibilities
of neural networks (artificial intelligence). Following
an introductory explanation of the science and history of
development, he delves deeper into the subject, covering
subjects such as: biological and mathematical neurons; artificial
neuron software project; and "training" a neural
network and speech recognition circuit
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Adaptive
Neural Network Control of Robotic Manipulators (World Scientific
Series in Robotics and Intelligent Systems , Vol 19)
S. S. Ge, Tong H. Lee, Christopher J. Harris, Tong Heng Lee
Synopsis
There has been considerable research interest in neural network
control of robots, and satisfactory results have been obtained
in solving some of the special issues associated with the
problems of robot control in an "on-and-off" fasion.
This text is dedicated to issues on adaptive control of robots
based on neural networks. The text has been tailored to give
a comprehensive study of robot dynamics, present structured
network models for robots, and provide systematic approaches
for neural network based adaptive controller design for rigid
robots, flexible joint robots, and robots in constraint motion.
Rigorous proof of the stability properties of adaptive neural
network controllers is provided. Simulation examples are also
presented to verify the effectiveness of the controllers,
and practical implementation issues associated with the controllers
are also discussed
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Fundamentals
of Neural Networks
Laurene Fausett
Book Description
An exceptionally clear, thorough introduction to neural networks
written at an elementary level. Written with the beginning
student in mind, the text features systematic discussions
of all major neural networks and fortifies the reader's understudy
with many examples.
Features and Benefits
Covers all major neural networks. Shows architectures in a
similar format for all nets - illustrating the similarities
and differences among them. Clarifies the differences in the
capabilities of the different networks by focusing on simple
problems - in many cases variations of a theme. Presents algorithms
in enough detail to facilitate the writing of computer programs.
Gives detailed examples of simple applications. Provides mathematical
development when it provides a guide to proper implementation
of a net. Includes exercises and 25 computer projects.
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The
Handbook of Brain Theory and Neural Networks
Michael A. Arbib (Editor)
Synopsis
This text charts the progress made in recent years in many
specific areas related to the following two questions: how
does the brain work?; and how can we build intelligent machines?
The handbook covers the entire range of topics involved in
brain theory and neural networks, from detailed models of
single neurons, analyses of different biological neural networks
and connectionist studies of psychology and language to mathematical
analyses of a variety of abstract neural networks and technological
applications of adaptive, artificial neural networks. The
main part of the text, Part Three, contains 267 articles by
leaders in the various fields, arranged alphabetically by
title. The first two parts are designed to help readers orient
themselves to this vast range of material. Part One introduces
several basic neural models, explains how the present study
of brain theory and neural networks integrates brain theory,
artificial intelligence and cognitive psychology, and provides
a tutorial on the concepts essential for understanding neural
networks as dynamic, adaptive systems. Part Two provides entry
into the many articles of Part Three through an introductory
"Meta-Map" and 23 road maps, each of which tours
all the Part Three articles on the chosen theme.
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The
Mind Within the Net
Manfred Spitzer
Synopsis
How does the brain work? How do billions of neurons bring
about ideas, sensations, emotions, and actions? Why do children
learn faster than elderly people? What can go wrong in perception,
thinking, learning and acting? Scientists use computer models
to help us to understand the most private and human experiences.
In this work, Manfred Spitzer shows how these models can fundamentally
change how we think about learning, creativity, thinking and
acting, as well as such matters as schools, retirement homes,
politics, and mental disorder. Neurophysiology has told us
a lot about how neurons work; neural network theory is about
how neurons work together to process information. Spitzer
provides a basic, nonmathematical introduction to neural networks
and their clinical applications. Part 1 explains the fundamental
theory of neural networks and how neural network models work.
Part 2 covers the principles of network functioning and how
computer simulations of neural networks have profound consequences
for our understanding of how the brain works. Part 3 covers
applications of network models (for example, to knowledge
representation, language, and mental disorders such as schizophrenia
and Alzheimer's disease) that shed light on normal and abnormal
states of mind. Finally, Spitzer concludes with his thoughts
on the ramifications of neural network for the understanding
of neuropsychology and human nature.
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Neural
Networks
Kevin Gurney
Synopsis
This key "user-friendly" feature notwithstanding,
the book provides a full level of explanation of the technical
aspects of the subject, which non-mathematical rivals usually
fail to provide, thereby leaving those areas obscure. Although
the study of neural networks is underpinned by ideas that
are often best described mathematically, the fundamentals
of the subject are accessible without the full mathematical
apparatus, as this treatment amply demonstrates. The book
provides comprehensive coverage of the following key areas:
artificial neurons as models of their real counterparts; the
geometry of network action in pattern space; gradient descent
methods, including back-propagation; associative memory and
Hopfield nets; and self-organization and feature maps. The
traditionally difficult topic of adaptive resonance theory
is clarified within a hierarchical description of its operation,
which disentangles features specific to separate levels of
discussion. Finally, a chapter is devoted to organizing the
study of neural networks in various ways, and it attempts
to overcome the general impression that it is a loose-knit
collection of structures and recipes. The primary aim of the
book is to provide an understanding of basic principles, but
it also includes several real-world examples to provide a
concrete focus. This should enhance its appeal to those involved
in the design, construction and management of networks in
commercial environments and who wish to improve their understanding
of network simulator packages. As a comprehensive and highly
accessible introduction to one of the most important topics
in cognitive and computer science, this volume should interest
a wide range of readers, both students and professionals,
in cognitive science, psychology, computer science and electrical
engineering
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The
Essence of Neural Networks
Robert Callan
Book Description
The Essence of Neural Networks is designed to be a first course
on neural networks for undergraduate students, with the mathematics
contained to a minimum. The book's main aim is to cover the
basic concepts, with the key neural network models explored
sufficiently deeply to allow a competent programmer to implement
the networks in a language of their choice.
The
first six chapters cover the main neural models that are
essential for a fundamental grounding in the subject, and
the last two chapters are devoted to an overview of some
of the links being developed between neural networks and
traditional AI.
Features
and Benefits
Self-test
questions and exercises for the students at the end of most
chapters.
A glossary
of terms.
Synopsis
The Essence of Neural Networks is designed to be a first
course on neural networks for undergraduate students, with
the mathematics kept to a minimum. The book's main aim is
to cover the basic concepts, with the key neural network
models explored sufficiently deeply to allow competent programmers
to implement the networks in a language of their choice.
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Linked:
The New Science of Networks
Albert-László Barabási
Synopsis
This work explores the new science of networks and their
impact on nature, business, medicine, and everyday life.
In the 1980s, James Gleick's "Chaos" introduced
the world to complexity. Now, Albert-Laszlo Barabsi's "Linked"
reveals the next major scientific leap: the study of networks.
We've long suspected that we live in a small world, where
everything is connected to everything else. Indeed, networks
are pervasive - from the human brain to the Internet to
the economy to our group of friends. These linkages, it
turns out, aren't random. All networks, to the great surprise
of scientists, have an underlying order and follow simple
laws. Understanding the structure and behaviour of these
networks will help us do some amazing things, from designing
the optimal organization of a firm to stopping a disease
outbreak before it spreads catastrophically. In this work,
Barabasi traces the development of this rapidly unfolding
science and introduces us to the scientists carrying out
this pioneering work. These "new cartographers"
are mapping networks in a wide range of scientific disciplines,
proving that social networks, corporations, and cells are
more similar than they are different, and providing important
new insights into the interconnected world around us. This
title provides a preview of the next century in science,
which should be transformed by these amazing discoveries.
Great
explanatory power!, 3 July, 2002
Reviewer: coert.visser@wxs.nl from Driebergen Netherlands
Nowadays, everybody talks about networks. Yet, what networks
really are and how they function, often remains rather vague
in conversations. This book offers great insight into the
evolution, the structure and the relevance of networks.
The author, Albert Barabási, himself a creative and
important
contributor to network science, makes the rapid and fascinating
advances made in this field comprehensible.
Our world is filled with complex networks, webs of highly
connected nodes. Not all nodes are equal, however. In fact,
in many real-world complex networks, there is a typical
hierarchy of nodes (called a POWERLAW DISTRIBUTION). This
means there are a few extremely well connected nodes (these
are called HUBS), there are quite a few moderately connected
nodes and there are large numbers of tiny nodes (having
very few connections to
other nodes). The Internet, for instance, has only several
hubs -like amazon.com and Yahoo - and countless tiny nodes
-like my own website :-(.
The structure of networks with a powerlaw distribution is
called a SCALEFREE TOPOLOGY. Such a scale free topology
is found in networks that 1)are GROWING (extra nodes and
links emerge), and 2) are characterised by PREFERENTIAL
ATTACHMENT (this means that some links are far more
likely to get linked than others). Preferential attachment,
is driven by two factors: 1) the number of links the node
already has (this is in fact the first mover advantage:
a nodes that has been there since the early evelopment of
the network gets the biggest chance to get connected), and
2) the node's fitness (for instance a new website offering
a truely unique service has an excellent chance to get many
links).
A fascinating characteristic of scale free networks is the
following. The density of the interconnectivity paradoxically
creates two properties at the same time: 1) ROBUSTNESS
(removing nodes will not easily lead to the breakdown of
the network, precisely because of the fact that all nodes
are connected. Only simultaneous removal of the largest
hubs will break down the network), and 2) VULNERABILITY
TO ATTACK (because of the fact that all nodes are indirectely
connected to each other failures, like viruses, can very
easily spread through the whole network. This fenomenon
is called 'cascading failures'.
Reading
this book made me realise that the recently acquired knowledge
about networks is revolutionizing many fields of science,
like biology, medical science and economics. Also, the practical
applications will be numerous, like protecting the internet,
fighting terrorist networks, finding a cure for cancer (!),
and developing new organizational forms.
A
brilliant overview of a fascinating new area of science,
25 June, 2002
Reviewer: Dr D Evans from Gloucestershire, United Kingdom
This is one of the clearest, most original and most exciting
popular-science books I have ever read. It manages to get
across the main points of network theory with a minimum
of technical jargon, and yet without oversimplification.
Many natural and artificial systems can profitably be viewed
as networks in which a number of nodes are connected by
links. For many years, the only networks that mathematicians
studied were so-called 'random graphs' in which all nodes
had more or less the same number of links. But in the late
1990s, when Albert Barabasi, a physicist at the University
of Nortre Dame, began to study real networks such as the
World Wide Web, he realised that they are rarely structured
like random graphs. In most real networks, it turns out,
the connectivity distribution decays as a power law - which
means that there is no such thing as a 'typical node'. Instead,
there are a few highly-connected nodes and many sparsely
connected nodes.
Since then, Barabasi and his research team at Notre Dame
have found many more examples of networks with this kind
of structure, from the metabolic network of protein-protein
interactions inside cells, to the social ties that link
CEOs in the 'old-boy network'. Despite being composed of
very different kinds of element, all these systems share
certain interesting properties simply because they have
similar structures. In other words, you can discover certain
things about a network simply by looking at its connectivity.
All this is fascinating in its own right, but it's even
better to get the message 'from the horse's mouth', rather
than from a journalist. I've followed the author's papers
in Nature with great interest over the past few years, but
it was nice to have an overview of the whole field of network
theory that stands back and presents the general context
as well as the specific details.
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