Matrix analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Robustness of radial basis functions
Neurocomputing
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
Evaluation of Neural Network Robust Reliability Using Information-Gap Theory
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Complete and partial fault tolerance of feedforward neural nets
IEEE Transactions on Neural Networks
The effects of quantization on multilayer neural networks
IEEE Transactions on Neural Networks
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Artificial recurrent neural network has been proved to be a valuable tool in modeling nonlinear dynamical systems. Robustness study of recurrent neural network is critical to its successful implementation. The goal of robustness study is to reduce the sensitivity of modeling capability to parametric uncertainties or make the network fault tolerant. In this study, an uncertainty propagation analysis is performed to quantify the robustness of a recurrent neural network output due to perturbations in its trained weights. An uncertainty propagation analysis-based robustness measure has been proposed accordingly and further compared with available performance loss-based and sensitivity matrix-based approaches. Results show that the proposed robustness measure approach is more efficient, generic, and flexible to quantify the robustness of a recurrent neural network.