Using neural networks to prediction of bulls' hides technological traits
The aim of study was adaptation of the artificial neural network to predict some of post-slaughter bulls hide parameters based on their body conformation. Material for the experiment was 105 bulls. Four variants of neural network were adopted which were different in length of learning (in 500, 1000, 1200 and 1500 epochs). The best variant appeared which was learning … full description
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- Articles
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- Collection of Scientific Papers, Faculty of Agriculture in České Budějovice. Series for Animal Sciences. -- ISSN 1212-558X. -- Roč. 22, č. 1 (2005), s. 91-99
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- Item Description:
- Grafy, tab.
- Physical Description:
- 11 il.
- Bibliography:
- Lit. na s. 99
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MARC
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| 072 | 7 | |a 004 |x Počítačová věda. Výpočetní technika. Informační technologie |2 Konspekt |9 23 | |
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| 245 | 0 | 0 | |a Using neural networks to prediction of bulls' hides technological traits / |c Krzysztof Adamczyk ...[et al.] |
| 300 | |b 11 il. | ||
| 500 | |a Grafy, tab. | ||
| 504 | |a Lit. na s. 99 | ||
| 520 | |a The aim of study was adaptation of the artificial neural network to predict some of post-slaughter bulls hide parameters based on their body conformation. Material for the experiment was 105 bulls. Four variants of neural network were adopted which were different in length of learning (in 500, 1000, 1200 and 1500 epochs). The best variant appeared which was learning in 1200 epochs. Therefore this network's variant was precisely evaluated. High conformity expressed by a coefficient of correlation has been proved for the following traits: surface of raw hide (r=0,86) and for a leather (r=0,88) and also for a weight of raw hide (r=0,81). However the neural network was ineffective in designation difference between a thickness in a butt leather standard point and a thickness in a point of fall of a thickness at shoulder blade region, because of a big prediction error which could be reduce by modification of network model and by increase number of the observations which took part in network learning process. |9 eng | ||
| 546 | |a Anglický text, anglický abstrakt | ||
| 650 | 0 | 7 | |a skot |2 agrovoc |
| 650 | 0 | 7 | |a býci |2 agrovoc |
| 650 | 0 | 7 | |a usně |2 agrovoc |
| 650 | 0 | 7 | |a zpracovatelská kvalita |2 agrovoc |
| 650 | 0 | 7 | |a technické vlastnosti |2 agrovoc |
| 650 | 0 | 7 | |a rozměry |2 agrovoc |
| 650 | 0 | 7 | |a plocha povrchu |2 agrovoc |
| 650 | 0 | 7 | |a hmotnost |2 agrovoc |
| 650 | 0 | 7 | |a prognózování |2 agrovoc |
| 650 | 0 | 7 | |a matematické modelování |2 agrovoc |
| 650 | 0 | 7 | |a počítače |2 agrovoc |
| 650 | 0 | 9 | |a CATTLE |
| 650 | 0 | 9 | |a BULLS |
| 650 | 0 | 9 | |a HIDES AND SKINS |
| 650 | 0 | 9 | |a PROCESSING QUALITY |
| 650 | 0 | 9 | |a TECHNICAL PROPERTIES |
| 650 | 0 | 9 | |a DIMENSIONS |
| 650 | 0 | 9 | |a SURFACE AREA |
| 650 | 0 | 9 | |a WEIGHT |
| 650 | 0 | 9 | |a FORECASTING |
| 650 | 0 | 9 | |a MATHEMATICAL MODELS |
| 650 | 0 | 9 | |a COMPUTERS |
| 653 | 0 | |a umělá neuronová síť | |
| 700 | 1 | |a Adamczyk, Krzysztof, |u Agricultural University of Cracow, Department of Cattle Breeding, Kraków, Poland |4 aut | |
| 700 | 1 | |a Zapletal, Piotr, |u Agricultural University of Cracow |4 aut | |
| 700 | 1 | |a Węglarz, Andrzej, |u Agricultural University of Cracow |4 aut | |
| 700 | 1 | |a Skrzyński, Grzegorz, |u Agricultural University of Cracow |4 aut | |
| 700 | 1 | |a Kuza, Katarzyna, |u Agricultural University of Cracow |4 aut | |
| 700 | 1 | |a Řehout, Václav, |d 1943- |u Jihočeská univerzita, Zemědělská fakulta, České Budějovice |7 jx20040622052 |4 aut | |
| 700 | 1 | |a Frelich, Jan, |d 1941- |u JČU, ZF, České Budějovice |7 xx0013568 |4 aut | |
| 700 | 1 | |a Voříšková, Jarmila, |d 1956- |u JČU, ZF, České Budějovice |7 uzp2006324635 |4 aut | |
| 773 | 0 | |t Collection of Scientific Papers, Faculty of Agriculture in České Budějovice. Series for Animal Sciences |x 1212-558X |g Roč. 22, č. 1 (2005), s. 91-99 |q 22:1 |9 2005 | |
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| 990 | |a RS | ||
| 998 | |a http://aleph.nkp.cz/F/?func=direct&doc_number=001103303&local_base=ANL | ||