查看更多>>摘要：In this paper, a Q-learning algorithm is proposed to solve the linear quadratic regulator problem of black box linear systems. The algorithm only has access to input and output measurements. A Luenberger observer parametrization is constructed using the control input and a new output obtained from a factorization of the utility function. An integral reinforcement learning approach is used to develop the Q-learning approximator structure. A gradient descent update rule is used to estimate on-line the parameters of the Q function. Stability and convergence of the Q-learning algorithm under the Luenberger observer parametrization is assessed using Lyapunov stability theory. Simulation studies are carried out to verify the proposed approach.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this study, the elimination of correlated errors with an unknown correlation in distributed fusion is investigated, and a consistent fusion method for distributed multi sensor systems is proposed. Unlike most existing fusion methods, the proposed method guarantees the consistency of fusion results without requiring system model parameters or adopting conservative strategies. First, a universal bijection is used to quantify the uncertainty in the estimates to be fused based on the entropy of the independent scalars. Second, the correlated errors caused by unknown mutual information and common process noise are treated as avoidable uncertainties. The avoidable uncertainty is then estimated by using a similarity function based on the Kullback-Leibler divergence. Finally, the avoidable uncertainty is separated from the fusion results by employing a conditional probability model to avoid correlated errors. This method is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method and the inverse covariance intersection method. The simulation results further verify the superiority of the proposed method in terms of the consistency, accuracy, and ability to limit cumulative errors in sequential fusion processes.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper studies the recovery probability of a state-of-the-art sparse recovery method, the optimization problem of YALL1, which has been rigorously used in face recognition, dense error correction, anomaly detection, etc. This work generalizes a theoretical work which is based on a special case of the optimization problem of YALL1. Furthermore, the new results cover more practical cases which do not fulfill the bouquet model proposed in the early work. The results also show that not only the special case but also some other cases of the optimization problem of YALL1; which fulfill certain conditions; can also recover any sufficiently sparse coefficient vector x when the fraction of the support of the error e is bounded away from 1 and the support of x is a very small fraction of its dimension m as m becomes large. The trade-off parameter k in the optimization problem of YALL1 allows the recovery probability to be optimally tuned than the special case. Experimental results also show that the optimization problem of YALL1 (the Eq. (7)) with primal augmented Lagrangian optimization technique outperforms the state-of-the-art sparse recovery methods using their corresponding optimization techniques in term of the speed. (c) 2021 Elsevier Inc. All rights reserved.
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