Artificial neural network feedback loop with on-line training

Stubberud, Stephen C.; Owen, Mark W.

doi:10.1109/isic.1996.556254

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Since the commutation relation of quadratures gives [ q, p] = ih, the uncertainty principle (14) reads…”

Section: State-space Representation Of An Opo System With Homodyne Me...mentioning

confidence: 99%

“…For example, the dual Kalman filter (dual-KF) method was first proposed in [11] for linear systems and then developed for nonlinear systems in [12], [13]. The joint extended Kalman filter (joint-EKF) [14], [15] combines the quantum state and the unknown pump power into a single joint vector. Thus, the combined system becomes nonlinear and the extended Kalman filter (EKF) is used to linearize the system.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the combined system becomes nonlinear and the extended Kalman filter (EKF) is used to linearize the system. Based on existing works [14], [15], [25], the joint-EKF is usually expected to be on average more economic than the dual-KF while it may suffer to the divergence problem. Moreover, the joint-EKF is often more sensitive to factors that increase the estimation error due to the approximation procedure imposed by the linearization of the EKF.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous estimation of parameters and the state of an optical parametric oscillator system

Qian¹,

Yokoyama²,

Dong³

et al. 2021

Preprint

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In this paper, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter method are employed to simultaneously estimate the system state and the pump power. Numerical examples demonstrate the effectiveness of the proposed algorithms.

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“…Since the commutation relation of quadratures gives [ q, p] = ih, the uncertainty principle (14) reads…”

Section: State-space Representation Of An Opo System With Homodyne Me...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous estimation of parameters and the state of an optical parametric oscillator system

Qian¹,

Yokoyama²,

Dong³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These works have considered problem of state estimation in linear systems. Joint-EKF was suggested to model unknown parameters in a model reference adaptive control framework [16]. Dual-EKF [17] was a nonlinear extension of the linear dual Kalman approach.…”

Section: Introductionmentioning

confidence: 99%

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

Safarinejadian¹,

Vafamand²

2015

Journal of Electrical Engineering and Technology

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-This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KR-JUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

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“…EKF to estimate the time-series and using these estimates to train a neural network via gradient descent. A joint EKF is used in [15] to model partially unknown dynamics in a model reference adaptive control framework. Furthermore, iterative EM approaches to the dual estimation problem have been investigated for radial basis function networks [16] and other nonlinear models [17]; see also Chapter 6.…”

mentioning

confidence: 99%