Sequential optimal experiment design for neural networks using multiple linearization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F10%3A00503729" target="_blank" >RIV/49777513:23520/10:00503729 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Sequential optimal experiment design for neural networks using multiple linearization
Original language description
Design of an optimal input signal in system identification using multi-layer perceptron network is treated. It is shown that utilizing the conditional probability density function of parameters for design of the input signal provides better results thancurrently used procedures based on prameter point estimates only. The conditional probability density function of parameters is approximated by a sum of normal distributions.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
73
Issue of the periodical within the volume
16-18
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
7
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
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