- Volume 16 Issue 1
In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.
- IEEE ASSP Magazine An introduction to computing with neural nets Lippmann, R.P.
- IEEE Trans. Neural Networks v.1 Sensitivity of feedforward neural networks to weight errors Stevenson, M.;Winter, R.;Windrow, B.
- IEEE Trans. Neural Networks v.3 Analysis of the effects of quantization in multilayer neural networks using a statistical model Xie, Y.;Jabri, M.A.
- IEEE Trans. Neural Networks v.3 Sensitivity analysis of multilayer perceptron with differentiable activation functions Choi, J.Y.;Choi, C.H.
- Proc. IJCNN'92 Beijing v.II An analysis on the classification performance of multilayer perceptrons Oh, S.H.;Lee, Y.
- Proc. IJCNN'90 San Diego v.I Analyses of the hidden units of backpropagation model by singular value decomposition Xue, Q.;Hu, Y.;Tompkins, W.T.
- IEEE Trans. Neural Networks Effect of nonlinear functions on correlation between weighted sums in multilayer perceptrons Oh, S.H.;Lee, Y.
- IEEE Communication Magazine Information theory, complexity, and neural networks Abu-Mostafa, Y.S.
- Proc. IJCNN'93 Nagoya v.III Analysis on the efficiency of pattern recognition layers using information measures Lee, Y.;Song, H.K.
- Probability, Random variables, and Stochastic Processes Papoulis, A.
Approximation of continuous functions on
$r^d$by linear combinations of shifted rotations of a sigmoid function with and without scaling Ito, Y.