## Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Volume 1With solid theoretical foundations and numerous potential applications, Blind Signal Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation: Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind Signal and Image Processing delivers an unprecedented collection of useful techniques for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signals and data. - Offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view
- Presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems
- Provides a guide to fundamental mathematics of multi-input, multi-output and multi-sensory systems
- Includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study
- Accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLAB user-friendly software packages are also provided
MATLAB is a registered trademark of The MathWorks, Inc.
By providing a detailed introduction to BSP, as well as presenting new results and recent developments, this informative and inspiring work will appeal to researchers, postgraduate students, engineers and scientists working in biomedical engineering, communications, electronics, computer science, optimisations, finance, geophysics and neural networks. |

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### Contents

Processing | 24 |

Solving a System of Algebraic Equations and Related Problems | 43 |

Inhibition Principles | 64 |

PrincipalMinor Component Analysis and Related Problems | 87 |

Blind Decorrelation and SOS for Robust Blind Identification | 129 |

Technique | 162 |

Statistical Signal Processing Approach to Blind Signal Extraction | 177 |

Kurtosis | 184 |

signals two fetal signals and two noise signals b Detailed | 297 |

Robust Techniques for BSS and ICA with Noisy Data | 305 |

with noise cancellation It is assumed that the reference | 311 |

Appendix A Cumulants in Terms of Moments | 333 |

Manifold | 376 |

Estimating Functions and Superefficiency | 383 |

Blind Filtering and Separation Using a StateSpace Approach | 423 |

Nonlinear State Space Models SemiBlind Signal Processing | 443 |

Reference Signals | 205 |

Natural Gradient Approach to Independent Component Analysis | 231 |

Locally Adaptive Algorithms for IC A and their Implementations | 273 |

learning algorithm 7 23 | 280 |

Gradient Approach | 451 |

Appendix Mathematical Preliminaries | 535 |

Glossary of Symbols and Abbreviations | 547 |

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### Common terms and phrases

additive noise algorithms for blind Amari apply approach assume batch blind deconvolution blind equalization blind separation blind signal blind source separation channel Chapter Cichocki coefficients constraints convergence convolutive cost function covariance matrix decorrelation defined denotes derived diagonal matrix distribution dynamical system eigenvalues equation equivariant extraction feed-forward finite impulse response FIR filters gradient descent ICA algorithm IEEE IEEE ICASSP IEEE Trans independent component analysis inverse iteration Kalman filter Kullback-Leibler divergence kurtosis learning rate learning rule Lie group linear matrix H method minimization mixing matrix multichannel blind deconvolution natural gradient algorithm natural gradient learning neural network nonholonomic nonsingular number of sources obtain on-line optimal orthogonal orthogonal matrix output signals parameters positive definite prewhitening problem Proc robust S.C. Douglas satisfies sensor signals separating matrix Signal Processing solution source signals statistical subspace Theorem transfer function unknown vector W(fc x(fc y(fc zero Zhang

### Popular passages

Page 486 - Space or time adaptive signal processing by neural network models," in Neural Networks for Computing: AIP Conference Proceedings 151, JS Denker, Ed., American Institute of Physics, New York, 1986.