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Hyper-Chaotic Neural Network Based on Newton Iterative Method and Its Application in Solving Load Flow Equations of Power System

机译:基于牛顿迭代法的超混沌神经网络及其在电力系统潮流方程求解中的应用

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Most of load flow equations of power system are multi-variable nonlinear equations set which need to know the initial value for solving, and the initial value is very difficult to choose. Neural network is a kind of highly complex nonlinear dynamic system and chaotic phenomenon is found in it. By utilizing the simulated annealing mechanism to eliminate transiently chaotic neuron, this paper presents a kind of chaotic neuron which can permanently sustain chaotic search. The topology of chaotic neural network composed of four chaotic neurons in which hyper-chaos exists is studied. For the first time, a novel method to find all solutions of nonlinear equations is proposed in which initial points are generated by hyper-chaotic neural network. The numerical example shows that the new method proposed in this paper is correct and effective, and it lays a good engineering foundation for finding all the solutions of load flow equations of power system.
机译:电力系统潮流方程大多数是多变量非线性方程组,需要知道求解的初值,很难选择初值。神经网络是一种高度复杂的非线性动力系统,在其中发现混沌现象。通过利用模拟退火机制消除瞬态混沌神经元,本文提出了一种可以永久维持混沌搜索的混沌神经元。研究了由四个超混沌存在的混沌神经元组成的混沌神经网络的拓扑结构。首次提出了一种寻找非线性方程所有解的新方法,该方法通过超混沌神经网络生成初始点。算例表明,本文提出的新方法正确有效,为寻找电力系统潮流方程的所有解奠定了良好的工程基础。

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