9,459 research outputs found
Consensus analysis of multiagent networks via aggregated and pinning approaches
This is the post-print version of of the Article - Copyright @ 2011 IEEEIn this paper, the consensus problem of multiagent nonlinear directed networks (MNDNs) is discussed in the case that a MNDN does not have a spanning tree to reach the consensus of all nodes. By using the Lie algebra theory, a linear node-and-node pinning method is proposed to achieve a consensus of a MNDN for all nonlinear functions satisfying a given set of conditions. Based on some optimal algorithms, large-size networks are aggregated to small-size ones. Then, by applying the principle minor theory to the small-size networks, a sufficient condition is given to reduce the number of controlled nodes. Finally, simulation results are given to illustrate the effectiveness of the developed criteria.This work was jointly supported by CityU under a research grant (7002355) and GRF funding (CityU 101109)
Distributed Coupled Multi-Agent Stochastic Optimization
This work develops effective distributed strategies for the solution of
constrained multi-agent stochastic optimization problems with coupled
parameters across the agents. In this formulation, each agent is influenced by
only a subset of the entries of a global parameter vector or model, and is
subject to convex constraints that are only known locally. Problems of this
type arise in several applications, most notably in disease propagation models,
minimum-cost flow problems, distributed control formulations, and distributed
power system monitoring. This work focuses on stochastic settings, where a
stochastic risk function is associated with each agent and the objective is to
seek the minimizer of the aggregate sum of all risks subject to a set of
constraints. Agents are not aware of the statistical distribution of the data
and, therefore, can only rely on stochastic approximations in their learning
strategies. We derive an effective distributed learning strategy that is able
to track drifts in the underlying parameter model. A detailed performance and
stability analysis is carried out showing that the resulting coupled diffusion
strategy converges at a linear rate to an neighborhood of the true
penalized optimizer
Collective Decision-Making in Ideal Networks: The Speed-Accuracy Tradeoff
We study collective decision-making in a model of human groups, with network
interactions, performing two alternative choice tasks. We focus on the
speed-accuracy tradeoff, i.e., the tradeoff between a quick decision and a
reliable decision, for individuals in the network. We model the evidence
aggregation process across the network using a coupled drift diffusion model
(DDM) and consider the free response paradigm in which individuals take their
time to make the decision. We develop reduced DDMs as decoupled approximations
to the coupled DDM and characterize their efficiency. We determine high
probability bounds on the error rate and the expected decision time for the
reduced DDM. We show the effect of the decision-maker's location in the network
on their decision-making performance under several threshold selection
criteria. Finally, we extend the coupled DDM to the coupled Ornstein-Uhlenbeck
model for decision-making in two alternative choice tasks with recency effects,
and to the coupled race model for decision-making in multiple alternative
choice tasks.Comment: to appear in IEEE TCN
Multilayer coevolution dynamics of the nonlinear voter model
We study a coevolving nonlinear voter model on a two-layer network.
Coevolution stands for coupled dynamics of the state of the nodes and of the
topology of the network in each layer. The plasticity parameter p measures the
relative time scale of the evolution of the states of the nodes and the
evolution of the network by link rewiring. Nonlinearity of the interactions is
taken into account through a parameter q that describes the nonlinear effect of
local majorities, being q = 1 the marginal situation of the ordinary voter
model. Finally the connection between the two layers is measured by a degree of
multiplexing `. In terms of these three parameters, p, q and ` we find a rich
phase diagram with different phases and transitions. When the two layers have
the same plasticity p, the fragmentation transition observed in a single layer
is shifted to larger values of p plasticity, so that multiplexing avoids
fragmentation. Different plasticities for the two layers lead to new phases
that do not exist in a coevolving nonlinear voter model in a single layer,
namely an asymmetric fragmented phase for q > 1 and an active shattered phase
for q
1, we can find two different transitions by increasing the plasticity
parameter, a first absorbing transition with no fragmentation and a subsequent
fragmentation transition
Cluster Consensus on Discrete-Time Multi-Agent Networks
Nowadays, multi-agent networks are ubiquitous in the real world. Over the last decade, consensus has received an increasing attention from various disciplines. This paper investigates cluster consensus for discrete-time multi-agent networks. By utilizing a special coupling matrix and the Kronecker product, a criterion based on linear matrix inequality (LMI) is obtained. It is shown that the addressed discrete-time multi-agent networks achieve cluster consensus if a certain LMI is feasible. Finally, an example is given to demonstrate the effectiveness of the proposed criterion
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