Int J Neural Syst - A neural observer with time-varying learning rate: analysis and applications.

Tópicos

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{ framework(1458) process(801) describ(734) }
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Resumo

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process.

Resumo Limpo

paper reduc order neural observ rono timevari learn rate propos propos scheme base discretetim recurr high order neural network rhonn train extend kalman filter ekfbas algorithm timevari learn rate design order improv learn neuron network presenc disturb paramet variat work includ stabil proof timevari learn applic develop observ illustr via simul nonlinear anaerob digest process

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