0000000000231091
AUTHOR
Jiri Bila
showing 3 related works from this author
Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications
2018
This paper is focused on weight adaptation stability analysis of static and dynamic neural units for prediction applications. The aim of this paper is to provide verifiable conditions in which the weight system is stable during sample-by-sample adaptation. The paper presents a novel approach toward stability of linear and higher-order neural units. A study of utilization of linear and higher-order neural units with the foundations on stability of the gradient descent algorithm for static and dynamic models is addressed.
Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units
2017
Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.
Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals
2017
This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.