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Abstract: This paper proposes blind source extraction methods based on several time-delay autocorrelations of primary sources, called MACBSE. The MACBSE approaches are batch fixed-point learning algorithms for extraction of source signals with linear autocorrelations. The fixed-point algorithms are very simple and do not need to choose any learning step sizes. Furthermore, the convergence properties of the algorithms are analyzed. Their efficiencies are demonstrated by extensive computer simulations. [Copyright &y& Elsevier]

Abstract: Previous papers have noted the difficulty in obtaining neural models which are stable under simulation when trained using prediction-error-based methods. Here the differences between series–parallel and parallel identification structures for training neural models are investigated. The effect of the error surface shape on training convergence and simulation performance is analysed using a standard algorithm operating in both training modes. A combined series–parallel/parallel training scheme is proposed, aiming to provide a more effective means of obtaining accurate neural simulation models. Simulation examples show the combined scheme is advantageous in circumstances where the solution space is known or suspected to be complex. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of blind source separation and presents a fixed-point nonlinear principal component analysis (NPCA) algorithm. It is a block-wise batch algorithm and gives an alternative perspective on existing adaptive online NPCA algorithms. Utilizing new activation functions that automatically satisfy a stability condition, the proposed algorithm can separate mixed signals with sub- and super-Gaussian source distributions. The efficiency is confirmed by extensive computer simulations on man-made sources as well as practical speech signals. [Copyright &y& Elsevier]