Adaptive blind signal processing-neural network approaches
S. Amari , A Cichocki
AbstractLearning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time
|Journal series||Proceedings of the IEEE, ISSN 0018-9219|
|Keywords in English||adaptive blind signal processing, adaptive equalisers, Adaptive equalizers, adaptive filtering, adaptive filters, adaptive learning algorithms, adaptive signal processing, Blind equalizers, convergence, deconvolution, Efficiency, equalization, feedforward neural nets, independent source signals, instantaneous blind separation, learning algorithms, learning (artificial intelligence), multichannel blind deconvolution, natural gradient approach, neural network approaches, neural networks, online adaptive nonlinear learning algorithms, recurrent neural nets, signal processing, signal processing algorithms, stability|
|Publication indicators||: 2006 = 3.686 (2) - 2007=4.939 (5)|
|Citation count*||555 (2015-03-25)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.