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Combinatorial development of solid c...

Baerns, M. (1934-{me_controlnum})

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Combinatorial development of solid catalytic materials = design of high-throughput experiments, data analysis, data mining /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Combinatorial development of solid catalytic materials/ Manfred Baerns, Martin Hole幯a.
Reminder of title:	design of high-throughput experiments, data analysis, data mining /
Author:	Baerns, M.
other author:	Hole幯a, Martin.
Published:	London :Imperial College Press ; : c2009.,
Description:	xi, 178 p. :ill. (some col.)
Subject:	Catalysis - Computer simulation. -
Online resource:	http://www.worldscientific.com/worldscibooks/10.1142/P620#t=toc
ISBN:	9781848163447 (electronic bk.)

Combinatorial development of solid catalytic materials = design of high-throughput experiments, data analysis, data mining /
Baerns, M.1934-{me_controlnum}

Combinatorial development of solid catalytic materialsdesign of high-throughput experiments, data analysis, data mining /[electronic resource] :Manfred Baerns, Martin Hole幯a. - London :Imperial College Press ;c2009. - xi, 178 p. :ill. (some col.) - Catalytic science series ;v. 7. - Catalytic science series ;v. 11..

Includes bibliographical references and index.

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks : automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.

Electronic reproduction.
Singapore :
World Scientific Publishing Co.,
2009.

System requirements: Adobe Acrobat Reader.

ISBN: 9781848163447 (electronic bk.)Subjects--Topical Terms:

872114
Catalysis
--Computer simulation.

LC Class. No.: QD505

Dewey Class. No.: 541.395

Combinatorial development of solid catalytic materials = design of high-throughput experiments, data analysis, data mining /
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300 $a xi, 178 p. : $b ill. (some col.)
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504 $a Includes bibliographical references and index.
520 $a The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks : automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
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