The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Adaptive blind signal processing-neural network approaches
Abstract
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 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.
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Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,205
Theory and Practice of Recursive Identification
Lennart Ljung, A., & Söderström, T. (1983) · Lund University Publications (Lund University) · Cited by 3,989
Natural Gradient Works Efficiently in Learning
Amari, S.-I. (1998) · Neural Computation · doi:10.1162/089976698300017746 · Cited by 2,681
Self-citationBlind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
Jutten, C., & Herault, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90079-x · Cited by 2,521
Independent Component Analysis of Electroencephalographic Data
Makeig, S., Bell, A. J., Jung, T., & B, T. J. S. A. (1995) · Cited by 1,957
A New Learning Algorithm for Blind Signal Separation
Амари, Ш.-И., Cichocki, A. S., & Yang, H. H. (1995) · Cited by 1,904
Self-citationDifferential-Geometrical Methods in Statistics
Amari, S.-I. (1985) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-5056-2 · Cited by 1,673
Equivariant adaptive source separation
Cardoso, J.-F., & Laheld, B. H. (1996) · IEEE Transactions on Signal Processing · doi:10.1109/78.553476 · Cited by 1,349
Neural networks for optimization and signal processing
Krzyzak, A. (1994) · Neurocomputing · doi:10.1016/0925-2312(94)90041-8 · Cited by 1,235
Cited by
Learning to Diagnose with LSTM Recurrent Neural Networks
Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015) · arXiv · doi:10.48550/arxiv.1511.03677 · Cited by 879
Metadata
- Authors
- Amari, S., & Cichocki, A.
- Venue
- Proceedings of the IEEE
- Published
- 1998
- Volume
- 86
- Issue
- 10
- Pages
- 2026-2048
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2126016605
- Cited by
- 466
- References
- 53
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- closed
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