Formal convergence analysis on deterministic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si198.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-regularization based mini-batch learning for RBF networks

Li, Zhaofeng; Leung, Chi-Sing; So, Hing Cheung

doi:10.1016/j.neucom.2023.02.012

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…For more detailed information about the advantages and efficiency of B-spline artificial neural networks, the interested reader is referred to the book [32]. The convergence properties of some gradient-based algorithms commonly utilized for training of some classes of artificial neural networks as used in this work can be examined in [33,34]. In this sense, the expected performance of the ANN depends on the correct delimitation of the training algorithm considering typical behavior of the system under analysis, starting with typical steady state conditions.…”

Section: Sliding-mode Differential-flatness Controlmentioning

confidence: 99%

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

et al. 2023

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A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.

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Section: Sliding-mode Differential-flatness Controlmentioning

confidence: 99%

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

et al. 2023

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“…The predictive ability of robust RBF networks has been compared only on a very small number of datasets with outliers [47] so that robust RBF networks started to penetrate to real applications only slowly. Moreover, the literature is void of regularized versions of robust RBF networks, although various regularization types have been often exploited for the plain (non-robust) RBF networks [27]. This paper is interested in highly robust regularized versions of RBF networks.…”

Section: Introductionmentioning

confidence: 99%

Highly robust training of regularized radial basis function networks

Kalina,

Vidnerová,

Janáček

2024

Kybernetika

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Radial basis function (RBF) networks represent established tools for nonlinear regression modeling with numerous applications in various fields. Because their standard training is vulnerable with respect to the presence of outliers in the data, several robust methods for RBF network training have been proposed recently. This paper is interested in robust regularized RBF networks. A robust inter-quantile version of RBF networks based on trimmed least squares is proposed here. Then, a systematic comparison of robust regularized RBF networks follows, which is evaluated over a set of 405 networks trained using various combinations of robustness and regularization types. The experiments proceed with a particular focus on the effect of variable selection, which is performed by means of a backward procedure, on the optimal number of RBF units. The regularized inter-quantile RBF networks based on trimmed least squares turn out to outperform the competing approaches in the experiments if a highly robust prediction error measure is considered.

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An optimized radial basis function neural network with modulation-window activation function

Lin,

Dai,

Mao

et al. 2023

Soft Comput

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Formal convergence analysis on deterministic ℓ1-regularization based mini-batch learning for RBF networks

Cited by 4 publications

References 54 publications

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

Highly robust training of regularized radial basis function networks

An optimized radial basis function neural network with modulation-window activation function

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