[1] 李士勇. 模糊控制、神经控制和智能控制论 [M]. 哈尔滨:哈尔滨工业大学出版社 1998.
[2] HASHEM S. Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions [A]. Baltimore 1992.
[3] ZURADA J M, MALINOWSKI A, USUI S. Perturbation method for deleting redundant inputs of perceptron networks [J]. Elsevier Science B V 1997, 14(02).
[4] WANG Xizhao, LI Chunguo. A new definition of sensitivity for RBFNN and Its applications to feature reduction [J]. Lecture Notes in Computer Science 2005, 3496.
[5] Jin Young Choi, Chong-Ho Choi. Sensitivity analysis of multilayer perceptron with differentiable activation functions [J]. IEEE Transactions on Neural Networks 1992, 1(1).
[6] YEUNG D S, SUN Xuequan. Using function approximation to analyze the sensitivity of MLP with antisymmetric squashing activation function [J]. IEEE Transactions on Neural Networks 2002, 13(01).
[7] Karayiannis Nicolaos B.. Reformulated radial basis neural networks trained by gradient descent [J]. IEEE Transactions on Neural Networks 1999, 3(3).
[8] Stevenson M., Winter R.. Sensitivity of feedforward neural networks to weight errors [J]. IEEE Transactions on Neural Networks 1990, 1(1).
[9] Cheng A.Y., Yeung D.S.. Sensitivity analysis of neocognitron [J]. IEEE transactions on systems, man and cybernetics, Part C. Applications and reviews: A publication of the IEEE Systems, Man, and Cybernetics Society 1999, 2(2).
[10] Piche S.W.. The selection of weight accuracies for Madalines [J]. IEEE Transactions on Neural Networks 1995, 2(2).
[11] NG W W Y, YEUNG D S, RAN Q. Statistical output sensitivity to input and weight perturbations of radial basis function neuron networks [A]. Twnisia 2002.
[12] ZENG Xiaoqin, YEUNG D S. Sensitivity analysis of multilayer perceptron to input and weight perturbations [J]. IEEE Transactions on Neural Networks 2001, 12(06).
[13] Xiaoqin Zeng, Daniel S. Yeung. A Quantified Sensitivity Measure for Multilayer Perceptron to Input Perturbation [J]. Neural computation 2003, 1(1).
[14] Shi D, Yeung DS, Gao J. Sensitivity analysis applied to the construction of radial basis function networks. [J]. Neural Networks: The Official Journal of the International Neural Network Society 2005, 7(7).
[15] NG W W Y, YEUNG D S. Input dimensionality reduction for Radial Basis Neural Network classification problems using sensitivity analysis [A]. 北京 2002.
[16] NG W W Y, CHANG R K C, YEUNG D S. Dimensionality reduction for denial of service detection problems using RBFNN output sensitivity [A]. 陕西西安 2003. |
