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The Locally Competitive Algorithm (LCA) is a continuous-time dynamical system designed to solve the problem of sparse approximation. This class of approximation problems plays an important role in producing state-of-the-art results in many signal processing and inverse problems, and implementing the LCA in analog VLSI may significantly improve the time and power necessary to solve these optimization...
This paper introduces an optimization based approach towards steady-state analysis of switched power electronic systems. The convergence performance of the optimization is improved by the estimation of the state variables in steady-state. Accurate state variable estimation can be achieved by previously simulating a set of multi-resolution approximations - averaged models of the original topological...
In order to improve the performance of switched reluctance driving system, it is necessary to build an accurate switched reluctance motor (SRM) model. In this paper, a nonlinear flux-linkage model and a torque model of SRM are presented by using the measured accurate flux-linkage data, torque data and nonlinear mapping ability of BP neural network, which is based on fast self-configuring algorithm...
Simulation optimization refers to the iterative procedure in search of the optimal parameter when the objective function can only be evaluated by stochastic simulation. STRONG (Stochastic Trust Region Response Surface Convergent Method) is a newly developed design-of-experiments based simulation optimization method. It incorporates the idea of trust region method (TRM) for deterministic optimization...
Fast convergence-rate, low computation complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient method and the unconstrained optimization algorithm to realize the supplement of their advantages, but also makes full use...
Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma where the controller has to sacrifice efficient performance for the sake of learning ldquobetterrdquo control strategies than the ones already known. In...
In this paper, by use of the properties of matrix measure, a set of linear and quadratic conditions is obtained which is sufficient for stabilizability of switched linear systems. These conditions are easily applicable because of their special form. So they are suitable to apply specially for higher order systems with several subsystems. Moreover, different system performances can also be achieved...
Optimal load distribution among the power distribution substation feeders is one of the most important and least expensive operational tools to lower the losses. For this purpose, the state of the network switches (open/closed) should be determined such that the load switched from the heavily loaded feeders to the lightly loaded ones and in the meantime the operational constraints and radial nature...
We study the problem of mean square error (MSE) transceiver design for point-to-multipoint transmission in multiuser multiple-input-multiple-output (MIMO) systems. We focus on four optimization problems: minimizing the maximum weighted layer-wise or user-wise MSE under a total power constraint; minimizing the total transmit power subject to a set of layer-wise or user-wise MSE requirements. The non-convexity...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
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