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Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons
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Author(s): Ivo Bukovsky (Czech Technical University in Prague, Czech Republic), Peter M. Benes (Czech Technical University in Prague, Czech Republic)and Martin Vesely (Czech Technical University in Prague, Czech Republic)
Copyright: 2020
Pages: 26
Source title:
Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
Source Author(s)/Editor(s): Gebrail Bekdaş (Istanbul University-Cerrahpaşa, Turkey), Sinan Melih Nigdeli (Istanbul University-Cerrahpaşa, Turkey)and Melda Yücel (Istanbul University-Cerrahpaşa, Turkey)
DOI: 10.4018/978-1-7998-0301-0.ch004
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Abstract
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.
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