Potentials of Quadratic Neural Unit for Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F13%3A00212026" target="_blank" >RIV/68407700:21220/13:00212026 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.igi-global.com/chapter/potentials-quadratic-neural-unit-applications/72791" target="_blank" >http://www.igi-global.com/chapter/potentials-quadratic-neural-unit-applications/72791</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-4666-2651-5.ch023" target="_blank" >10.4018/978-1-4666-2651-5.ch023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Potentials of Quadratic Neural Unit for Applications

Popis výsledku v původním jazyce

The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applicationsfor their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relativelyvery strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implement

Název v anglickém jazyce

Potentials of Quadratic Neural Unit for Applications

Popis výsledku anglicky

The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applicationsfor their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relativelyvery strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implement

Druh

C - Kapitola v odborné knize

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Advances in Abstract Intelligence and Soft Computing

ISBN

9781466626829

Počet stran výsledku

12

Strana od-do

343-354

Počet stran knihy

456

Název nakladatele

IGI Global

Místo vydání

Hershey, Pennsylvania

Kód UT WoS kapitoly

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