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After an introduction to renewable energy technologies, the authors
present computational intelligence techniques for optimizing the
manufacture of related technologies, including solar concentrators.
In particular the authors present new applications for their neural
classifiers for image and pattern recognition. The book will be of
interest to researchers in computational intelligence, in
particular in the domain of neural networks, and engineers engaged
with renewable energy technologies.
Micromechanical manufacturing based on microequipment creates new
possibi- ties in goods production. If microequipment sizes are
comparable to the sizes of the microdevices to be produced, it is
possible to decrease the cost of production drastically. The main
components of the production cost - material, energy, space
consumption, equipment, and maintenance - decrease with the scaling
down of equipment sizes. To obtain really inexpensive production,
labor costs must be reduced to almost zero. For this purpose, fully
automated microfactories will be developed. To create fully
automated microfactories, we propose using arti?cial neural
networks having different structures. The simplest perceptron-like
neural network can be used at the lowest levels of microfactory
control systems. Adaptive Critic Design, based on neural network
models of the microfactory objects, can be used for manufacturing
process optimization, while associative-projective neural n- works
and networks like ART could be used for the highest levels of
control systems. We have examined the performance of different
neural networks in traditional image recognition tasks and in
problems that appear in micromechanical manufacturing. We and our
colleagues also have developed an approach to mic- equipment
creation in the form of sequential generations. Each subsequent
gene- tion must be of a smaller size than the previous ones and
must be made by previous generations. Prototypes of ?rst-generation
microequipment have been developed and assessed.
Computational intelligence encompasses a wide variety of techniques
that allow computation to learn, to adapt, and to seek. That is,
they may be designed to learn information without explicit
programming regarding the nature of the content to be retained,
they may be imbued with the functionality to adapt to maintain
their course within a complex and unpredictably changing
environment, and they may help us seek out truths about our own
dynamics and lives through their inclusion in complex system
modeling. These capabilities place our ability to compute in a
category apart from our ability to erect suspension bridges,
although both are products of technological advancement and reflect
an increased understanding of our world. In this book, we show how
to unify aspects of learning and adaptation within the
computational intelligence framework. While a number of algorithms
exist that fall under the umbrella of computational intelligence,
with new ones added every year, all of them focus on the
capabilities of learning, adapting, and helping us seek. So, the
term unified computational intelligence relates not to the
individual algorithms but to the underlying goals driving them.
This book focuses on the computational intelligence areas of neural
networks and dynamic programming, showing how to unify aspects of
these areas to create new, more powerful, computational
intelligence architectures to apply to new problem domains.
Computational intelligence encompasses a wide variety of techniques
that allow computation to learn, to adapt, and to seek. That is,
they may be designed to learn information without explicit
programming regarding the nature of the content to be retained,
they may be imbued with the functionality to adapt to maintain
their course within a complex and unpredictably changing
environment, and they may help us seek out truths about our own
dynamics and lives through their inclusion in complex system
modeling. These capabilities place our ability to compute in a
category apart from our ability to erect suspension bridges,
although both are products of technological advancement and reflect
an increased understanding of our world. In this book, we show how
to unify aspects of learning and adaptation within the
computational intelligence framework. While a number of algorithms
exist that fall under the umbrella of computational intelligence,
with new ones added every year, all of them focus on the
capabilities of learning, adapting, and helping us seek. So, the
term unified computational intelligence relates not to the
individual algorithms but to the underlying goals driving them.
This book focuses on the computational intelligence areas of neural
networks and dynamic programming, showing how to unify aspects of
these areas to create new, more powerful, computational
intelligence architectures to apply to new problem domains.
Micromechanical manufacturing based on microequipment creates new
possibi- ties in goods production. If microequipment sizes are
comparable to the sizes of the microdevices to be produced, it is
possible to decrease the cost of production drastically. The main
components of the production cost - material, energy, space
consumption, equipment, and maintenance - decrease with the scaling
down of equipment sizes. To obtain really inexpensive production,
labor costs must be reduced to almost zero. For this purpose, fully
automated microfactories will be developed. To create fully
automated microfactories, we propose using arti?cial neural
networks having different structures. The simplest perceptron-like
neural network can be used at the lowest levels of microfactory
control systems. Adaptive Critic Design, based on neural network
models of the microfactory objects, can be used for manufacturing
process optimization, while associative-projective neural n- works
and networks like ART could be used for the highest levels of
control systems. We have examined the performance of different
neural networks in traditional image recognition tasks and in
problems that appear in micromechanical manufacturing. We and our
colleagues also have developed an approach to mic- equipment
creation in the form of sequential generations. Each subsequent
gene- tion must be of a smaller size than the previous ones and
must be made by previous generations. Prototypes of ?rst-generation
microequipment have been developed and assessed.
In 1901, Karl Pearson invented Principal Component Analysis
(PCA). Since then, PCA serves as a prototype for many other tools
of data analysis, visualization and dimension reduction:
Independent Component Analysis (ICA), Multidimensional Scaling
(MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The
book starts with the quote of the classical Pearson definition of
PCA and includes reviews of various methods: NLPCA, ICA, MDS,
embedding and clustering algorithms, principal manifolds and SOM.
New approaches to NLPCA, principal manifolds, branching principal
components and topology preserving mappings are described as well.
Presentation of algorithms is supplemented by case studies, from
engineering to astronomy, but mostly of biological data: analysis
of microarray and metabolite data. The volume ends with a tutorial
"PCA and K-means decipher genome." The book is meant to be useful
for practitioners in applied data analysis in life sciences,
engineering, physics and chemistry; it will also be valuable to PhD
students and researchers in computer sciences, applied mathematics
and statistics.
Social media data contains our communication and online sharing,
mirroring our daily life. This book looks at how we can use and
what we can discover from such big data: Basic knowledge (data
& challenges) on social media analytics Clustering as a
fundamental technique for unsupervised knowledge discovery and data
mining A class of neural inspired algorithms, based on adaptive
resonance theory (ART), tackling challenges in big social media
data clustering Step-by-step practices of developing unsupervised
machine learning algorithms for real-world applications in social
media domain Adaptive Resonance Theory in Social Media Data
Clustering stands on the fundamental breakthrough in cognitive and
neural theory, i.e. adaptive resonance theory, which simulates how
a brain processes information to perform memory, learning,
recognition, and prediction. It presents initiatives on the
mathematical demonstration of ART's learning mechanisms in
clustering, and illustrates how to extend the base ART model to
handle the complexity and characteristics of social media data and
perform associative analytical tasks. Both cutting-edge research
and real-world practices on machine learning and social media
analytics are included in the book and if you wish to learn the
answers to the following questions, this book is for you: How to
process big streams of multimedia data? How to analyze social
networks with heterogeneous data? How to understand a user's
interests by learning from online posts and behaviors? How to
create a personalized search engine by automatically indexing and
searching multimodal information resources? .
Computational Learning Approaches to Data Analytics in Biomedical
Applications provides a unified framework for biomedical data
analysis using varied machine learning and statistical techniques.
It presents insights on biomedical data processing, innovative
clustering algorithms and techniques, and connections between
statistical analysis and clustering. The book introduces and
discusses the major problems relating to data analytics, provides a
review of influential and state-of-the-art learning algorithms for
biomedical applications, reviews cluster validity indices and how
to select the appropriate index, and includes an overview of
statistical methods that can be applied to increase confidence in
the clustering framework and analysis of the results obtained.
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