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A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by(More)
—In this paper, we exploit the geographic oppor-tunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in wireless sensor networks (WSNs). Recent work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant(More)
—Indoor localization is essential to enable location-based services in wireless pervasive computing environment. In recent years, WiFi fingerprint-based localization has received considerable attention due to its deployment practicability. In order to achieve on-the-fly localization, WiFi receivers (e.g., mobile phones or laptops) being located need to scan(More)
Duane's retraction syndrome (DRS) is a complex congenital eye movement disorder caused by aberrant innervation of the extraocular muscles by axons of brainstem motor neurons. Studying families with a variant form of the disorder (DURS2-DRS), we have identified causative heterozygous missense mutations in CHN1, a gene on chromosome 2q31 that encodes(More)
A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the(More)
In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational(More)
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of(More)