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The book provides a bottom-up approach to understanding how a
computer works and how to use computing to solve real-world
problems. It covers the basics of digital logic through the lens of
computer organization and programming. The reader should be able to
design his or her own computer from the ground up at the end of the
book. Logic simulation with Verilog is used throughout, assembly
languages are introduced and discussed, and the fundamentals of
computer architecture and embedded systems are touched upon, all in
a cohesive design-driven framework suitable for class or
self-study.
The book provides a bottom-up approach to understanding how a
computer works and how to use computing to solve real-world
problems. It covers the basics of digital logic through the lens of
computer organization and programming. The reader should be able to
design his or her own computer from the ground up at the end of the
book. Logic simulation with Verilog is used throughout, assembly
languages are introduced and discussed, and the fundamentals of
computer architecture and embedded systems are touched upon, all in
a cohesive design-driven framework suitable for class or
self-study.
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.
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