Titlebook: Intelligent Control and Automation; International Confer De-Shuang Huang,Kang Li,George William Irwin Book 2006 Springer-Verlag Berlin Heid

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An Efficient Blind SIMO Channel Identification Algorithm Via Eigenvalue Decomposition not require the input signal to be independent and identical distribution, and even the input signal can be non-stationary. Compared with Least-Square Approach, the new algorithm is more robust to the overestimation of channel order. Finally, the experiments demonstrate the good performance of the proposed algorithm.

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Lecture Notes in Control and Information Scienceshttp://image.papertrans.cn/i/image/469545.jpg

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A Unified Framework of Morphological Associative Memories morphological associative memories (EFMAM), etc., are extremely new artificial neural networks. They have many attractive advantages such as unlimited storage capacity, one-short recall speed and good noise-tolerance to erosive or dilative noise. Although MAM, FMAM and EFMAM are different and easil

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A New Speech Denoising Method Based on WPD-ICA Feature Extraction not be directly applied in ICA feature extraction. In this paper, we propose a new method using wavelet packet decomposition (WPD) as preprocessing for single channel data. Wavelet packet coefficients (WPCs) provide multi-channel data as input data to learn ICA basis vectors. Furthermore we project

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A New Step-Adaptive Natural Gradient Algorithm for Blind Source Separationtability. The proposed BSS algorithms mainly derived from the Gaussian probability density function and statistical independence. A new adaptive method is proposed in the paper. The method uses the temporal predictability as cost function which is not studied as much as other generic differences bet