Adaptive blind signal processing-neural network approaches

S. Amari, A. Cichocki
1998 Proceedings of the IEEE  
Learning 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 recent 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
more » ... s 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. Keywords-Blind deconvolution and equalization, blind separation of signals, independent component analysis (ICA), natural gradient learning, neural networks, self-adaptive learning rates, unsupervised adaptive learning algorithms.
doi:10.1109/5.720251 fatcat:jg337aeuxnd3rec634qd3qjfde