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Universal approximation of nonlinear system predictions in sigmoid activation functions using artificial neural networks

机译:使用人工神经网络的S型激活函数中非线性系统预测的通用逼近

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The sigmoid activation function cast-off to convert the equal of activation of units (neurons) in the output indicator. There are a numeral of mutual tasks in activation with the use of artificial neural networks (ANN). The maximum communal use of manifold functions to Multi Layered Perceptron (MLP) and the transmission of professions in research and engineering. However, given the wide range of problematic fields are applied in the MLP, it is interesting to suspect that the detailed difficulties that require one or exact activation utilities of the group. The aim of this paper is to consider the presentation of buildings MLP generalized who appeared deployment algorithm by numerous dissimilar functions to activate the sigmoid neurons of the hidden and output layers.
机译:SIGMOID激活功能抛弃,转换输出指示器中的单位(神经元)的相同等同。使用人工神经网络(ANN)激活,在激活中有一个数字。歧管功能的最大公共用途与多层的Perceptron(MLP)和研究和工程专业传输。然而,鉴于MLP应用了广泛的有问题领域,可疑是有趣的是,可疑是需要该组的一个或精确激活实用程序的详细困难。本文的目的是考虑建筑物MLP的呈现,通过许多不同的功能出现了部署算法来激活隐藏和输出层的矩形神经元。

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