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Experimental Design for Combinatorial and High Throughput Materials Development
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SYSNO ASEP 0405205 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Artificial Neural Networks in Catalyst Development. Chapter 10 Title Umělé neuronové sítě při vývoji katalyzátorů Author(s) Holeňa, Martin (UIVT-O) SAI, RID
Baerns, M. (DE)Source Title Experimental Design for Combinatorial and High Throughput Materials Development / Cawse J.N.. - New Jersey : John Wiley and Sons, 2003 - ISBN 0-471-20343-2 Pages s. 163-202 Number of pages 40 s. Language eng - English Country US - United States Keywords artificial neural networks ; multilayer perceptrons ; nonlinear dependency ; approximation ; network training ; knowledge extraction Subject RIV IN - Informatics, Computer Science Next source Other public resources DOI q Annotation In this paper, main principles of employing multilayer perceptrons for the approximation of unknown functions are outlined, and another possible use of multilayer perceptrons in combinatorial catalysis is indicated – their use for the extraction of knowledge from experimental catalytic input and output data. To counterbalance the abstractness of the subject, the method is illustrated by applying multilayer perceptrons to data on catalyst composition and catalytic results in the oxidative dehydrogenation of propane to propene. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2008
Number of the records: 1