Linear and Nonlinear Noise Monitoring in Coherent Systems Using Fast BER Measurement and Neural Networks

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

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IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING

International Journal of Neural Systems ◽

10.1142/s0129065791000303 ◽

1991 ◽

Vol 02 (04) ◽

pp. 331-339 ◽

Cited By ~ 10

Author(s):

Jiahan Chen ◽

Michael A. Shanblatt ◽

Chia-Yiu Maa

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Nonlinear Programming ◽

Penalty Function ◽

New Combination ◽

Optimal Point ◽

Linear And Nonlinear Programming ◽

Linear And Nonlinear ◽

Simulation Results

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.

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Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

International Journal of Psychological Studies ◽

10.5539/ijps.v11n4p1 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Tobias de Taillez ◽

Florian Denk ◽

Bojana Mirkovic ◽

Birger Kollmeier ◽

Bernd T. Meyer

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Linear Models ◽

Transfer Functions ◽

Event Related Potentials ◽

Stimulus Presentation ◽

Related Potentials ◽

Linear And Nonlinear ◽

Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

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