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In this paper, we propose a new consensus protocol in the form of distributed model predictive control (DMPC) in order to deal with the consensus problem of first-order discrete-time multi-agent systems. Equivalent form of general numerical solution of DMPC is presented by solving optimization problem, and necessary and sufficient convergence conditions of our proposed DMPC are obtained by using the...
By introducing multi-agent systems, a consensus based distributed optimization algorithm is considered in this paper. The state of each agent is to be controlled to reach a consensus on the global optimal set. From control point of view, the control input of each agent is designed based on neighbours' state information and local cost functions. Necessary and sufficient conditions on the convergence...
Semi-Markov conditional random fields (semi-CRFs) are usually trained with maximum a posteriori (MAP) criterion which adopts the 0/1 cost for measuring the loss of misclassification. In this paper, based on our previous work on handwritten Chinese/Japanese text recognition (HCTR) using semi-CRFs, we propose an alternative parameter learning method by minimizing the risk, in which the misclassification...
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