Memory-Efficient Fully Coupled Filtering Approach for Observational Model Building

Mussa, Hamse Y.; Glen, Robert C.

doi:10.1109/tnn.2010.2041067

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Only results on the boundedness of the expected quadratic error are reported in the literature [46]. In spite of its poor theoretical foundation, a number of successful results concerning the application of the EKF to neural network training are available [24]- [28]. Finally, it is worth noting that, in our context and likewise for line-search descent methods, the Newton and quasi-Newton methods are not ensured to converge to a global optimum since the optimization problem we have to solve is not convex.…”

Section: B Newton-based Optimizationmentioning

confidence: 99%

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Optimal Control of Propagating Fronts by Using Level Set Methods and Neural Approximations

Alessandri

Bagnerini

Gaggero

2019

IEEE Trans. Neural Netw. Learning Syst.

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We address the optimal control of level sets associated with the solution of the normal flow equation. The problem consists in finding the normal velocity to the front described by a certain level set in such a way to minimize a given cost functional. First, the considered problem is shown to admit a solution on a suitable space of functions. Then, since in general it is difficult to solve it analytically, an approximation scheme that relies on the extended Ritz method is proposed to find suboptimal solutions. Specifically, the control law is forced to take on a neural structure depending nonlinearly on a finite number of parameters to be tuned, i.e., the neural weights. The selection of the optimal weights is performed with two different approaches. The first one employs classical line-search descent methods, while the second one is based on a quasi-Newton optimization that can be regarded as neural learning based on the extended Kalman filter. Compared with line-search methods, such an approach reveals to be successful with a reduced computational effort and an increased robustness with respect to the trapping into local minima, as confirmed by simulations in both two and three dimensions.

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Section: B Newton-based Optimizationmentioning

confidence: 99%

“…The second one is based on an optimization derived from the Newton method. In practice, the selection of the weights is accomplished by means of an EKF learning procedure [24]- [28]. Both approaches require to compute the gradient of the cost w.r.t.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Propagating Fronts by Using Level Set Methods and Neural Approximations

Alessandri

Bagnerini

Gaggero

2019

IEEE Trans. Neural Netw. Learning Syst.

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Adaptive control and signal processing literature survey (No. 19)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk ADAPTIVE SYSTEMSAhn H-S, Chen Y. Periodic adaptive learning compensation of state-dependent disturbance. IET Control Theory and Applications 2010; 4(4):529-538. Angelov P, Kordon A. Adaptive inferential sensors based on evolving fuzzy models. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(2):529-539. Bechlioulis C, Rovithakis G. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(5):1220-1226. Bekiaris-Liberis N, Krstic M. Delay-adaptive feedback for linear feedforward systems. Systems and Control Letters 2010; 59(5):277-283. Chen M, Ge S, How B. Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities. IEEE Transactions on Neural Networks 2010; 21(5):796-812. Choi K, Yoo S, Park J, Choi Y. Adaptive formation control in absence of leader's velocity information. IET Control Theory and Applications 2010; 4(4):521-528. Fuentes D, Feng Y, Elliott A, Shetty A, McNichols R, Oden J, Stafford R. Adaptive real-time bioheat transfer models for computer-driven MR-guided laser induced thermal therapy. IEEE Transactions on Biomedical Engineering 2010; 57(5):1024-1030. Hu C, Yao B, Wang Q. Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations. Automatica 2010; 46(4):701-707. Jiménez-Fabián R, Alvarez-Icaza L. An adaptive observer for a shear building with an energy-dissipation device. Control Engineering Practice 2010; 18(4):331-338. Kaylani A, Georgiopoulos M, Mollaghasemi M, Anagnostopoulos G, Sentelle C, Zhong M. An adaptive multiobjective approach to evolving ART architectures. IEEE Transactions on Neural Networks 2010; 21(4):529-550. Lin W, Ma H. Synchronization between adaptively coupled systems with discrete and distributed time-delays. IEEE Transactions on Automatic Control 2010; 55(4): 819-830. Ma L, Cao C, Hovakimyan N, Dobrokhodov V, Kaminer I. Adaptive vision-based guidance law with guaranteed performance bounds. Journal of Guidance, Control, and Dynamics 2010; 33(3):834-852. Olm J, Ramos G, Costa-Castelló R. Adaptive compensation strategy for the tracking/rejection of signals with time-varying frequency in digital repetitive control systems. Journal of Process Control 2010; 20(4):551-558. Peng S, Hwang W-L. Null space pursuit: an operator-based approach to adaptive signal separation. IEEE Transact...

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Extended Kalman filtering to design optimal controllers of fronts generated by level set methods

Alessandri

Bagnerini

Gaggero

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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The computational issues involved in the solution of optimal control problems of propagating fronts described by level sets motivate the search for effective optimization algorithms. In this paper, we attack the problem of optimal control of moving fronts by searching for an approximate solution method that is computationally feasible and robust to local minima trapping. The presence of many local minima is a crucial difficulty one encounters in dealing with such a problem. Following previous results, we use the extended Ritz method to find approximate solutions. This approach consists in adopting a control law with fixed structure that depends nonlinearly on a number of parameters to be suitably chosen. To overcome the local minima issue, we propose to optimize the weights for the level set optimal control by a recursive minimization based on the extended Kalman filter (EKF). As compared with techniques based on gradient-descent methods, the EKF optimization turns out to be successful to reduce computational burden and increase robustness with respect to local minima trapping, as shown by simulation results in a test case involving a change of topology.

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Memory-Efficient Fully Coupled Filtering Approach for Observational Model Building

Cited by 3 publications

References 20 publications

Optimal Control of Propagating Fronts by Using Level Set Methods and Neural Approximations

Optimal Control of Propagating Fronts by Using Level Set Methods and Neural Approximations

Adaptive control and signal processing literature survey (No. 19)

Extended Kalman filtering to design optimal controllers of fronts generated by level set methods

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