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Neural Network Applications in Control
Neural Network Applications in Control
Knygos.lt klubas Knygos.lt nariams
195,57 €
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279,39 €
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Neural networks are an exciting technology of growing importance in real industrial situations, particularly in control and systems. This book aims to give a detailed appreciation of the use of neural nets in these applications; it is aimed particularly at those with a control or systems background who wish to gain an insight into the technology in the context of real applications. The book introduces a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis functio…

Neural Network Applications in Control (el. knyga) (skaityta knyga) | knygos.lt

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Neural networks are an exciting technology of growing importance in real industrial situations, particularly in control and systems. This book aims to give a detailed appreciation of the use of neural nets in these applications; it is aimed particularly at those with a control or systems background who wish to gain an insight into the technology in the context of real applications.

The book introduces a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks, as well as the more usual multi-layer perception back-propagation networks. It begins by describing the basic principles and some essential design features, then goes on to examine in depth several application studies illustrating a range of advanced approaches to the topic.

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Neural networks are an exciting technology of growing importance in real industrial situations, particularly in control and systems. This book aims to give a detailed appreciation of the use of neural nets in these applications; it is aimed particularly at those with a control or systems background who wish to gain an insight into the technology in the context of real applications.

The book introduces a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks, as well as the more usual multi-layer perception back-propagation networks. It begins by describing the basic principles and some essential design features, then goes on to examine in depth several application studies illustrating a range of advanced approaches to the topic.

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