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Identification of switched linear systems has received considerable attention during the past few years. Since the problem is generically NP-Hard, the majority of existing algorithms are based on heuristics or relaxations. Therefore, it is crucial to check the validity of the identified models against additional experimental data. This paper addresses the problem of model (in)validation for multi-input...
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...
The reactive power optimal control is a multi-objective optimization problem with discrete variables in nature. The conventional control schemes are usually “time-based” which lack the flexibility for online control. Based on “event-driven” strategy, a hybrid control approach is proposed. The principle of hybrid control, system design ideas and implementation methods are introduced. The application...
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...
Considering the radial network constraint of the distribution system, an improved Ant colony optimization (ACO) algorithm combined with stochastic spanning tree algorithm is proposed in this paper. The proposed method applied in handling the service restoration problem in power distribution systems. In order to improve the searching efficiency, the behaviors of the ants are controlled in the feasible...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
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...
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