To Contemplate Quantitative and Qualitative Water Features by Neural Networks Method.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F02%3A06030043" target="_blank" >RIV/67985807:_____/02:06030043 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

To Contemplate Quantitative and Qualitative Water Features by Neural Networks Method.

Original language description

An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. A Stuttgart neural simulator SNNS and a multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. Aperceptron network has been constructed, which is capable of an accurate forecast of the next day runoff based on the runoff and rainfall values from previous day.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/IAB1030006" target="_blank" >IAB1030006: Alternative learning procedures for reedforward neural networks</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2002

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Rostlinná výroba

ISSN

0370-663X

e-ISSN

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Volume of the periodical

48

Issue of the periodical within the volume

7

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

6

Pages from-to

322-326

UT code for WoS article

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EID of the result in the Scopus database

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