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曾晓勤, Yingfeng Wang
LETTER Communicated by Andries P. Engelbrecht Neural Computation 18, 2854-2877 (2006),-0001,():
-1年11月30日
The sensitivity of a neural network’s output to its input and weight perturbations is an important measure for evaluating the network’s performance. In this letter, we propose an approach to quantify the sensitivity of Madalines. The sensitivity is defined as the probability of output deviation due to input and weight perturbations with respect to overall input patterns. Based on the structural characteristics of Madalines, a bottomup strategy is followed, along which the sensitivity of single neurons, that is, Adalines, is considered first and then the sensitivity of the entire Madaline network. By means of probability theory, an analytical formula is derived for the calculation of Adalines’ sensitivity, and an algorithm is designed for the computation of Madalines’ sensitivity. Computer simulations are run to verify the effectiveness of the formula and algorithm. The simulation results are in good agreement with the theoretical results.
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曾晓勤, Xiaoqin
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 6, NOVEMBER 2001,-0001,():
-1年11月30日
An
Multilayer
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曾晓勤, Xiaoqin
,-0001,():
-1年11月30日
This paper presents an approach to determine the relevance of individual input attributes for trained Multilayer Perceptrons (MLPs). To reflect the impact of an input attribute on the output of an MLP, the relevance is aimed at representing the output sensitivity of the MLP to the attribute variation. The sensitivity is defined as the mathematical expectation of output deviations of an MLP due to its input deviation with respect to overall input patterns. The basic idea for the introduction of such a relevance measure is that a well-trained MLP can capture salient features of the problem it deals with and thus become more sensitive to those input attributes that make more contributions to the MLP’s behavior. The relevance can be employed as a relative criterion for assessing individual input attributes. The results from the experiments on two typical problems demonstrate the effectiveness of the relevance in identifying irrelevant input attribute.
Neural
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曾晓勤, Xiaoqin
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 2, MARCH 2006,-0001,():
-1年11月30日
In
Adaline,
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