Classical discrete-time adaptive controllers provide asymptotic stabilization. While the original adaptive controllers did not handle noise or unmodelled dynamics well, redesigned versions were proven to have some tol...
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Classical discrete-time adaptive controllers provide asymptotic stabilization. While the original adaptive controllers did not handle noise or unmodelled dynamics well, redesigned versions were proven to have some tolerance;however, neither exponential stabilization nor a bounded noise gain is typically proven. Here we consider the first order case and prove that if the original, ideal, projection algorithm is used in the estimation process (subject to the common assumption that the plant parameters lie in a convex, compact set and that the parameter estimates are restricted to that set), then it guarantees linear-like convolution bounds on the closed loop behaviour, which implies exponential stability and a bounded noise gain, as well an easily proven tolerance to unmodelled dynamics and plant parameter variation. (C) 2017 Elsevier B.V. All rights reserved.
We consider a search algorithm for the output distribution that achieves the channel capacity of a discrete memoryless channel. We will propose an algorithm by iterated projections of an output distribution onto affin...
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We consider a search algorithm for the output distribution that achieves the channel capacity of a discrete memoryless channel. We will propose an algorithm by iterated projections of an output distribution onto affine subspaces in the set of output distributions. The problem of channel capacity has a similar geometric structure as that of smallest enclosing circle for a finite number of points in the Euclidean space. The metric in the Euclidean space is the Euclidean distance and the metric in the space of output distributions is the Kullback-Leibler divergence. We consider these two problems based on Amari's a-geometry. Then, we first consider the smallest enclosing circle in the Euclidean space and develop an algorithm to find the center of the smallest enclosing circle. Based on the investigation, we will apply the obtained algorithm to the problem of channel capacity.
In this paper, a distributed adaptive control method is considered for a class of discrete-time multi-agent systems with nonlinearity and uncertainty. Each agent is affected by its neighbors, and there is a hidden age...
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In this paper, a distributed adaptive control method is considered for a class of discrete-time multi-agent systems with nonlinearity and uncertainty. Each agent is affected by its neighbors, and there is a hidden agent as the leader in multi-agent systems who knows the desired reference signal. However, other agents are aware of neither the reference signal nor the existence of the hidden leader and leadership. In order to deal with uncertainty, a criteria function for each agent, which is consist of a weighted square combination of state errors and parameter errors with timevarying weighting factor, is adopted. By minimizing the criteria function, we propose a projection algorithm for each agent to estimate unknown parameters. Furthermore, we design a distributed adaptive controller for each agent using the information of its neighbors. Under the distributed adaptive control, the rigorous mathematical proof is presented to demonstrate that all the agents ultimately track the desired reference signal. Finally, simulation results are given to illustrate the theoretical results.
This paper presents parallelization strategies for the implementation of imaging algorithms for synthetic aperture radar (SAR). Great emphasis is placed on time-domain based algorithms, namely the Global Backprojectio...
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ISBN:
(纸本)9781538631690
This paper presents parallelization strategies for the implementation of imaging algorithms for synthetic aperture radar (SAR). Great emphasis is placed on time-domain based algorithms, namely the Global Backprojection algorithm (GBP) and its accelerated version, the Fast Factorized Backprojection algorithm (FFBP). Multi-core platforms are selected for implementation as some combine good performance results with moderate power consumption. The implemented algorithms support several types of parallelization, as the stages of the algorithms can be handled sequentially or interleaved. For the GBP algorithm three different data distribution schemes are investigated. For the FFBP algorithm a successive stage calculation method is compared with a combined calculation method. The performance is exemplary evaluated on the low cost/energy, yet powerful multi-core platform Odroid-XU4. All parallelization strategies show an almost linear speed-up with the number of used cores. Even though a specific multi-core platform is investigated, the design decisions are applicable for general multi-core architectures.
Classical discrete-time adaptive controllers provide asymptotic stabilization. While the original adaptive controllers did not handle noise or unmodelled dynamics well, redesigned versions were proven to have some tol...
详细信息
ISBN:
(纸本)9781509021826
Classical discrete-time adaptive controllers provide asymptotic stabilization. While the original adaptive controllers did not handle noise or unmodelled dynamics well, redesigned versions were proven to have some tolerance;however, exponential stabilization and a bounded gain on the noise was rarely proven. Here we consider a classical pole placement adaptive controller using the original projection algorithm rather than the commonly modifed version;we impose the assumption that the plant parameters lie in a convex, compact set and that the parameter estimates are projected onto that set at every step. We demonstrate that the closed-loop system exhibits very desireable closed-loop behaviour: there are linear-like convolution bounds on the closed loop behaviour, which confers exponential stability and a bounded noise gain. We emphasize that there is no persistent excitation requirement of any sort.
In this paper, we propose a double projection algorithm for a generalized variational inequality with a multi-valued mapping. Under standard conditions, our method is proved to be globally convergent to a solution of ...
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In this paper, we propose a double projection algorithm for a generalized variational inequality with a multi-valued mapping. Under standard conditions, our method is proved to be globally convergent to a solution of the variational inequality problem. Moreover, we present a unified framework of projection-type methods for multi-valued variational inequalities. Preliminary computational experience is also reported. (C) 2011 Elsevier Inc. All rights reserved.
It is shown in this paper that under strict complementarity condition, a linear programming problem can be solved by a single orthogonal projection operation onto the cone generated by rows of constraint matrix and co...
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It is shown in this paper that under strict complementarity condition, a linear programming problem can be solved by a single orthogonal projection operation onto the cone generated by rows of constraint matrix and corresponding right-hand sides. The efficient projection procedure with the finite termination is provided and computational experiments are reported.
The article focuses on the family of projection and reflection methods which form the basis for a class of iterative algorithms which can be used to solve the feasibility problem. It discusses the feasibility problem ...
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The article focuses on the family of projection and reflection methods which form the basis for a class of iterative algorithms which can be used to solve the feasibility problem. It discusses the feasibility problem framework and fundamental projection-type algorithms, as well as the cyclic Douglas-Rachford method. Moreover, it focuses on a theory in the presence of convex constraint sets.
It is important for improving the prediction accuracy of short-term output of grid-connected photovoltaic (PV) systems for improving the safety, stability, and economic operation of power system. A short-term PV power...
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It is important for improving the prediction accuracy of short-term output of grid-connected photovoltaic (PV) systems for improving the safety, stability, and economic operation of power system. A short-term PV power probability forecasting method based on Gaussian process regression (GPR) is proposed under this study. This method classifies the weather type into five categories according to the modified clearness index proposed by Perez et al. Then, the orthogonal locality preserving projection (OLPP) algorithm is used to extract the feature vectors of meteorological variables. Based on the extracted feature vectors of meteorological variables, establish GPR model under different weather types, which were compared with the adaboost-BP neural network. The simulation results show that OLPP-GPR based on modified clearness index can be utilised to accurately predict PV power.
We propose a hierarchical (BV, G) variational decomposition model for multiscale texture extraction in this paper, which can offers a hierarchical, separated representation of image texture in different scales. The pr...
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We propose a hierarchical (BV, G) variational decomposition model for multiscale texture extraction in this paper, which can offers a hierarchical, separated representation of image texture in different scales. The proposed hierarchical decomposition is obtained by replacing the fixed scale parameter of the A(2)BC model with a varying sequence. Some properties of this hierarchical decomposition are presented and its convergence is proved. We adopt Euclidean projection algorithm to solve this hierarchical decomposition model numerically. In addition, we use this hierarchical decomposition to achieve the multiscale texture extraction. The performance of the proposed model is demonstrated with both synthetic and real images. (C) 2015 Elsevier GmbH. All rights reserved.
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