Signal processing in a nonlinear, nongaussian, and nonstationary world

Authors:
Simon Haykin
Affiliations:
McMaster University, Hamilton, Ontario, Canada
Venue:
Nonlinear Speech Modeling and Applications
Year:
2005

Citing 6
Cited 1

Adaptive filter theory

Adaptive filter theory
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Real-time computing without stable states: a new framework for neural computation based on perturbations

Neural Computation
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)

Inference in Hidden Markov Models (Springer Series in Statistics)
Stochastic correlative learning algorithms

IEEE Transactions on Signal Processing

Some notes on nonlinearities of speech

Nonlinear Speech Modeling and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article discusses three specific issues of particular interest in the study of nonlinear dynamical systems: Bayesian estimation, exemplified by particle filtering; Learning in recurrent neural networks; Correlative learning, exemplified by the ALOPEX algorithm. By and large, the discussion is of a philosophical nature.