阈值函数训练神经网络的灵敏度

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI:10.1109/ICNN.1994.374316

Sang-Hoon Oh, Youngjik Lee

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摘要

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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Sensitivity of trained neural networks with threshold functions

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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Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

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