39,108 research outputs found
Modeling of complex-valued Wiener systems using B-spline neural network
In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate Bspline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the GaussāNewton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches
Multiplex PI-Control for Consensus in Networks of Heterogeneous Linear Agents
In this paper, we propose a multiplex proportional-integral approach, for
solving consensus problems in networks of heterogeneous nodes dynamics affected
by constant disturbances. The proportional and integral actions are deployed on
two different layers across the network, each with its own topology. Sufficient
conditions for convergence are derived that depend upon the structure of the
network, the parameters characterizing the control layers and the node
dynamics. The effectiveness of the theoretical results is illustrated using a
power network model as a representative example.Comment: 13 pages, 6 Figures, Preprint submitted to Automatic
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)
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