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Vector quantization (VQ) is an efficient technique for data compression and has been successfully used in various applications. The methods most commonly used to generate a codebook are the Linde, Buzo, Gray (LBG) algorithm, fuzzy vector quantization (FVQ) algorithm, Kekre's Fast Codebook Generation (KFCG) algorithm, discrete cosine transform based (DCT-based) codebook generation method, and k-principle component analysis (K-PCA) algorithm. However, if the separation boundaries in codebook generation are nonlinear, their performance can degrade fast. In this paper, we present a kernel fuzzy learning (KFL) algorithm, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method, to create a codebook automatically. Experiments with real data show that the proposed algorithm is more efficient in its performance compared to that of the LBG, FVQ, KFCG, and DCT-based method, and to the K-PCA algorithm.

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Blind source separation (BSS) of continuous-time chaotic signals from a linear mixture is addressed in this brief. It is assumed that the functional forms of the generating systems of chaotic signals are known, and the parameters of the generating systems and the mixture matrix are unknown. The problem of determining the parameters and the mixture matrix is formulated as an optimization one. A fast random search (FRS) algorithm is, therefore, proposed. Experimental results demonstrate that the FRS algorithm can solve the indeterminacy problem in BSS and show the separability of mixed signals in a high noise background. Index Terms-Blind source separation (BSS), continuous-time chaotic signals, fast random search (FRS).

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