Wavelet domain nonlinear filtering for evoked potential signal enhancement
Computers and Biomedical Research
Smoothly adjustable denoising using a priori knowledge
Signal Processing - Signal processing in UWB communications
Filtering for a class of nonlinear discrete-time stochastic systems with state delays
Journal of Computational and Applied Mathematics
An impulsive noise reduction agent for rigid body motion data using B-spline wavelets
Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
Multivariate denoising using wavelets and principal component analysis
Computational Statistics & Data Analysis
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
International Journal of Computational Science and Engineering
Penalized least squares for smoothing financial time series
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Information Sciences: an International Journal
Hi-index | 12.05 |
For the subjectivity of lifting wavelet coefficients selection, an adaptive noise reduction method is proposed for chaotic signals corrupted by nonstationary noises. Here, wavelet coefficients including coarse approximation and detail information are obtained by dual-lifting wavelet transform. The coarse parts are handled by the singular spectrum analysis, whereas the detail parts are analyzed combining with gradient decent algorithm in neural networks for the adaptive choice of wavelet coefficients. The chaotic signals generated by Lorenz model as well as the observed monthly series of sunspots are respectively applied for simulation analysis. The experimental results show a dramatic improvement of the proposed method, the advantages of which include the simple of achieving, the small reconstruction error and the efficiency for the noisy chaotic signals.