Spikes: exploring the neural code
Spikes: exploring the neural code
Pulsed Neural Networks
Automatica (Journal of IFAC)
Communication constraints in the average consensus problem
Automatica (Journal of IFAC)
Distributed Average Consensus using Probabilistic Quantization
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
On quantized consensus by means of gossip algorithm: part i: convergence proof
ACC'09 Proceedings of the 2009 conference on American Control Conference
On quantized consensus by means of gossip algorithm: part i: convergence proof
ACC'09 Proceedings of the 2009 conference on American Control Conference
On quantized consensus by means of gossip algorithm: part ii: convergence time
ACC'09 Proceedings of the 2009 conference on American Control Conference
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This paper concerns the average consensus problem with the constraint of quantized communication between nodes. A broad class of algorithms is analyzed, in which the transmission strategy, which decides what value to communicate to the neighbours, can include various kinds of rounding, probabilistic quantization, and bounded noise. The arbitrariness of the transmission strategy is compensated by a feedback mechanism which can be interpreted as a self-inhibitory action. The result is that the average of the nodes state is not conserved across iterations, and the nodes do not converge to a consensus; however, we show that both errors can be made as small as desired. Bounds on these quantities involve the spectral properties of the graph and can be proved by employing elementary techniques of LTI systems analysis.