Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00196111" target="_blank" >RIV/68407700:21220/12:00196111 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.igi-global.com/chapter/fundamentals-higher-order-neural-networks/71797" target="_blank" >http://www.igi-global.com/chapter/fundamentals-higher-order-neural-networks/71797</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-4666-2175-6.ch006" target="_blank" >10.4018/978-1-4666-2175-6.ch006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Popis výsledku v původním jazyce

In this chapter, we provide fundamental principles of higher order neural units (HONUs) and higher order neural networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU. Except the high quality of nonlinear approximation of static HONUs, the capability of dynamic HONUs for modeling of dynamic systems is shown and compared to conventional recurrent neural networks when a practical learning algorithm is used. Also, the potential of continuous dynamic HONUs to approximate high dynamic-order systems is discussed as adaptable time delays can be implemented. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective for modeling of systems.

Název v anglickém jazyce

Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Popis výsledku anglicky

In this chapter, we provide fundamental principles of higher order neural units (HONUs) and higher order neural networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU. Except the high quality of nonlinear approximation of static HONUs, the capability of dynamic HONUs for modeling of dynamic systems is shown and compared to conventional recurrent neural networks when a practical learning algorithm is used. Also, the potential of continuous dynamic HONUs to approximate high dynamic-order systems is discussed as adaptable time delays can be implemented. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective for modeling of systems.

Druh

C - Kapitola v odborné knize

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Artificial Higher Order Neural Networks for Modeling and Simulation

ISBN

978-1-4666-2175-6

Počet stran výsledku

31

Strana od-do

103-133

Počet stran knihy

454

Název nakladatele

IGI Global

Místo vydání

Hershey, Pennsylvania

Kód UT WoS kapitoly

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