Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Comparison of approximate methods for handling hyperparameters
Neural Computation
Bayesian MLP neural networks for image analysis
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Improvements in Speech Synthesis
Improvements in Speech Synthesis
Linear Prediction of Speech
Nonlinear Synthesis of Vowels in the LP Residual Domain with a Regularized RBF Network
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Nonlinear Long-Term Prediction of Speech Signals1
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
Synthesis and coding of continuous speech with the nonlinear oscillator model
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Nonlinear dynamic modeling of the voiced excitation for improved speech synthesis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
IEEE Transactions on Signal Processing
FRBF neural network and new Smith predictor for wireless networked control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Identification of nonlinear oscillator models for speech analysis and synthesis
Nonlinear Speech Modeling and Applications
| Hi-index | 0.00 |
We examine Bayesian learning of a regularization factor and the noise level of radial basis function (RBF) networks in the framework of nonlinear time-series prediction and system modeling. A Bayesian trained RBF network is applied in an autonomous recursive prediction model (oscillator model) for regenerating time-series generated by the Lorenz system and speech signals. The oscillator model is able to capture the invariant measures of the Lorenz system for high enough SNR, and to reproduce the voiced part of speech signals.