This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only ...
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This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communicationconstraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.
This paper is concerned with the distributed H, fusion filtering problem (DHFFP) for a class of networked multi-sensor fusion systems with communication bandwidth constraints. Due to the limited bandwidth, only finite...
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This paper is concerned with the distributed H, fusion filtering problem (DHFFP) for a class of networked multi-sensor fusion systems with communication bandwidth constraints. Due to the limited bandwidth, only finite-level quantized sensor messages are sent to the fusion center, and multiple finite-level logarithmic quantizers are introduced to describe the above quantization strategy. In this sense, the DHFFP is inherent the co-design of the fusion parameters and quantization parameters. With the aid of the discrete-time bounded real lemma, the co-design problem is converted into a convex optimization problem over all the aforementioned parameters, which can be easily solved by standard software packages. It turns out that the performance of the designed distributed fusion filter is superior to that of each local quantized estimate. Finally, a numerical example is given to show the effectiveness of the proposed method. (C) 2013 Elsevier B.V. All rights reserved.
This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with communication bandwidth constraints. To satisfy finite communication ban...
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This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with communication bandwidth constraints. To satisfy finite communicationbandwidth, only partial components of the local vector estimation signals are transmitted to the fusion center (FC) at each time step, where multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the untransmitted components of each local estimate at the FC end, and a recursive distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense. Moreover, a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error of the designed DFKF is minimal at each time step. An illustrative example is given to show the effectiveness of the proposed methods.
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