Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation with an unknown number of sources
Neural Computation
Blind source separation using order statistics
Signal Processing
Nonlinear underdetermined blind signal separation using Bayesian neural network approach
Digital Signal Processing
Blind sparse source separation using cluster particle swarm optimization technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
General approach to blind source separation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
Fuzzy-based learning rate determination for blind source separation
IEEE Transactions on Fuzzy Systems
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
Fast probabilistic self-structuring of generalized single-layer networks
IEEE Transactions on Neural Networks
A robust approach to independent component analysis of signals with high-level noise measurements
IEEE Transactions on Neural Networks
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an assumption could not be held in many advanced applications. Thus, this paper proposes the adaptive neural algorithm (ANA) which designs and associates several auto-adjust mechanisms to challenge these advanced BSS problems. The first implementation is the on-line estimator of source numbers improved from the cross-validation technique. The second is the adaptive structure neural network that combines feed-forward architecture and the self-organized criterion. The last is the learning rate adjustment in order to enhance efficiency of learning. The validity and performance of the proposed algorithm are demonstrated by computer simulations, and are compared to algorithms with state of the art. From the simulation results, these have been confirmed that the proposed ANA performed better separation than others in static BSS cases and is feasible for dynamic BSS cases.