Описание: This book aims to extend existing works on consensus of multi-agent systems systematically. This framework relates containment to consensus and overcomes the difficulty of construction of a containment error. This book serves as a reference to the main research issues and results on consensus of multi-agent systems.
Описание: This book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network.
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