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This article investigates the neural-network-based (NN-based) set-membership filtering issue for nonlinear systems. In order to lighten the network transmission burden and avoid data collisions, the weighted try-once-discard (WTOD) protocol is employed to regulate the signal transmission process, which provides higher transmission priority to the most needed data. Considering the data discarding problem of the WTOD protocol, a novel event-triggered compensation mechanism is proposed to compensate the measurement output processed by the WTOD protocol, thereby improving the filtering performance. Next, considering the nonlinear dynamics of the system and the unknown-but-bounded (UBB) noise interference, an NN-based set-membership filter is designed to solve the state estimation problem. In a unified set-membership framework, an neural-network (NN) weight adaptive tuning law and a state estimation algorithm are designed. Sufficient conditions are derived for the existence of the adaptive NN parameters and the NN-based set-membership filter, and two optimization problems are put forward to seek the optimal NN parameters and filtering parameters that make the filter performance optimal. Finally, illustrative examples demonstrate the effectiveness of the proposed compensation mechanism and filtering algorithm.

As the successor to Sunway TaihuLight, the new Sunway supercomputer has ultra-high computing capacity, but the unique heterogeneous architecture presents performance optimization challenges for High Performance Computing (HPC) applications. Performance modeling is an effective way to discover the performance bottlenecks and then improve the performance of HPC applications. Existing performance modeling techniques do not work well on large-scale HPC applications due to high overhead and low accuracy, and are not suitable for the heterogeneous architecture due to a lack of support for multi-resource parameters. To address the above challenges, we propose an automatic multi-parameter performance modeling method for HPC applications on the new Sunway supercomputer. First, a lightweight performance profiling method is proposed to achieve low overhead performance profiling. Then, performance models with multiple resource parameters based on the Fourier neural operator are built, achieving high prediction accuracy and generalization ability. Finally, the Fourier neural operator is extended on the new Sunway supercomputer to realize the performance modeling automatically. Experimental results show that the average prediction error is less than 10% and the average overhead is less than 4%, and the results are superior to the baselines.

This article studies set-membership estimation for discrete-time linear time-varying systems with unknown-but-bounded uncertainties. An ellipsoidal set-membership filtering method with a new estimator is proposed. Based on the S-procedure, the proposed set-membership filtering is formulated as an optimization problem in terms of linear matrix inequalities. Moreover, it is shown that the optimal ellipsoids estimated by two existing set-membership filtering approaches are equivalent to the optimal estimation results obtained by two special cases of the proposed method, respectively. The proposed estimator has more design parameters which can be effectively optimized to improve the estimation accuracy. Simulation results are given to show the performance of the proposed method.

Abstract This paper is concerned with the strong γ c - γ cl H ∞ stabilization problem for networked control systems (NCSs) subject to denial of service (DoS) attacks, which are common attack behaviors that affect the packet transmission of measurement or control signals. The purpose of the problem under consideration is to design a stable dynamic output feedback (DOF) controller (strong stabilizing controller) with the prescribed H ∞ performance norm bound γ c to tolerate multiple packet dropouts caused by DoS attacks, such that, the closed-loop system is mean-square stable and captures the H ∞ disturbance attenuation norm bound γ cl. Based on the Lyapunov functional and the stochastic control approach, some sufficient conditions with the form of matrix inequalities for the existence of the desired stable DOF controller are established. Then, by an orthogonal complement space technique, the controller gain is parameterized. Next, an iterative linear matrix inequality (LMI) algorithm is developed to obtain the controller gain. Finally, the usefulness of the proposed method is indicated by a numerical simulation example. [ABSTRACT FROM AUTHOR]