Sensitivity of trained neural networks with threshold functions

Sang-Hoon Oh, Youngjik Lee
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Abstract

In this paper, we derive the sensitivity of single hidden-layer networks with threshold functions, called "Madaline", as a function of the trained weights, the input pattern, and the variance of weight perturbation or the bit error probability of the binary input pattern. The derived results are verified with a simulation of the Madaline recognizing handwritten digits. Our result show that the sensitivity in a trained network is far different from that of networks with random weights.<>
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阈值函数训练神经网络的灵敏度
本文推导了具有阈值函数(Madaline)的单隐层网络的灵敏度是训练权值、输入模式和权值扰动方差或二进制输入模式的误码概率的函数。通过Madaline识别手写体数字的仿真验证了所得结果。我们的结果表明,训练后的网络的灵敏度与随机权值的网络有很大的不同
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