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Projection type neural network and its convergence analysis

机译:投影型神经网络及其收敛性分析

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摘要

为优化问题的神经网络有的设计类型为更少参数,低寻找空格尺寸和简单结构在另外的网络上有利。在这篇论文,由适当地构造 Lyapunov 精力功能,当被用来优化在一个关上的凸的集合上定义的连续地可辨的凸的功能时,我们证明了这个网络的全球集中。结果解决网络的广泛的适用性。几个数字例子被给验证网络的效率。
机译:Projection type neural network for optimization problems has advantages over other networks for fewer parameters , low searching space dimension and simple structure. In this paper, by properly constructing a Lyapunov energy function, we have proven the global convergence of this network when being used to optimize a continuously differentiable convex function defined on a closed convex set. The result settles the extensive applicability of the network.Several numerical examples are given to verify the efficiency of the network.

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