Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems

Kumar, Rajesh; Srivastava, Smriti; Gupta, J. R. P.; Mohindru, Amit

doi:10.1016/j.isatra.2018.11.027

Cited by 23 publications

(6 citation statements)

References 54 publications

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“…As a result, various neural networks have been proposed to model nonlinear systems by utilizing such a feed-through structure recently. In [10], a temporally local recurrent radial basis function network was proposed that contains self-feedback loops and a weighted linear feedthrough from input to the output structure, where tests on five different nonlinear systems were made. Conversely, in [11], a novel context-layered recurrent pi-sigma neural network (CLRPSNN) is proposed to involve a context layer that feeds the delayed output of the pi-sigma neural network (PSNN) to the input layer through a weighted linear layer.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Hybrid Modeling of Francis Hydroturbine Dynamics via a Neural Controlled Differential Equation Approach

Wang,

Yin,

Jiang

2023

IEEE Access

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This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Section: Related Workmentioning

confidence: 99%

Real-Time Hybrid Modeling of Francis Hydroturbine Dynamics via a Neural Controlled Differential Equation Approach

Wang,

Yin,

Jiang

2023

IEEE Access

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“…Although the RBF networks have been applied in various fields because of their nonlinear approximation capabilities, in some practical applications, they require a large number of central nodes to acquire satisfactory modeling accuracy [32,33]. To get more accessible networks, the locally linear RBF (LLRBF) networks were presented.…”

Section: The Problem Formulationmentioning

confidence: 99%

Two-stage extended recursive gradient algorithm for locally linear RBF-based autoregressive models with colored noises

2022

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“…In the work by Kumar et al (2019), a recurrent type of radial basis function network (RBFN) is developed and applied to modeling and control of nonlinear systems. To have a fast learning of weight vectors, an adaptive learning rate is employed in the weight update equation.…”

Section: Introductionmentioning

confidence: 99%

A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory

Hamza,

Zribi,

Farhat

2023

Transactions of the Institute of Measurement and Control

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This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (RTRL). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrange multiplier theory is used as the optimization tool. The contribution of this paper is the integration of the LST into the Lagrange constraint function to synthesize a new analytical adaptation gain rate satisfying the asymptotic stability of the NE and providing good emulation performances. To confirm the good performances and the convergence ability of the proposed adaptation algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

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Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems

Cited by 23 publications

References 54 publications

Real-Time Hybrid Modeling of Francis Hydroturbine Dynamics via a Neural Controlled Differential Equation Approach

Real-Time Hybrid Modeling of Francis Hydroturbine Dynamics via a Neural Controlled Differential Equation Approach

Two-stage extended recursive gradient algorithm for locally linear RBF-based autoregressive models with colored noises

A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory

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