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Chaotic simulated annealing neural network with decaying chaotic noise and its application in economic load dispatch of power systems

机译:混沌噪声的混沌模拟退火神经网络及其在电力系统经济负荷分配中的应用

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Based on chaotic neural network (CNN), chaotic simulated annealing model with decaying chaotic noise (CSA-DCN) is presented. This model combines some advantages of Hopfield neural network (HNN) and simulated annealing (SA) algorithm, and decaying chaotic noise produced by iterated functions of logistic map is inducted into this model, which means it can be used to solve many multidimensioned, discrete, nonconvex, nonlinear constrained optimization problems, such as economic load dispatch (ELD) of power systems. Involved the transmission loss and valve point effect (VPE), the CSA-DCN model is applied to solve the ELD problem, the simulation results of three examples show that the CSA-DCN model for the ELD problem is versatile, robust and efficient.
机译:基于混沌神经网络(CNN),提出了具有衰减混沌噪声的混沌模拟退火模型(CSA-DCN)。该模型结合了Hopfield神经网络(HNN)和模拟退火(SA)算法的优势,并将逻辑映射的迭代函数产生的衰减混沌噪声引入该模型,这意味着它可用于解决许多多维,离散,非凸,非线性约束的优化问题,例如电力系统的经济负荷分配(ELD)。涉及传递损失和阀点效应(VPE),将CSA-DCN模型用于解决ELD问题,三个实例的仿真结果表明,针对ELD问题的CSA-DCN模型具有通用性,鲁棒性和高效性。

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