Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F19%3A00333452" target="_blank" >RIV/68407700:21220/19:00333452 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.igi-global.com/chapter/introduction-and-application-aspects-of-machine-learning-for-model-reference-adaptive-control-with-polynomial-neurons/238139" target="_blank" >https://www.igi-global.com/chapter/introduction-and-application-aspects-of-machine-learning-for-model-reference-adaptive-control-with-polynomial-neurons/238139</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-7998-0301-0.ch004" target="_blank" >10.4018/978-1-7998-0301-0.ch004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

Popis výsledku v původním jazyce

This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

Název v anglickém jazyce

Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

Popis výsledku anglicky

This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

20304 - Aerospace engineering

Projekt

<a href="/cs/project/EF16_019%2F0000826" target="_blank" >EF16_019/0000826: Centrum pokročilých leteckých technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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