论文标题
将多目标优化应用于盲源分离
Application of multi-objective optimization to blind source separation
论文作者
论文摘要
专家系统解决了信号处理中的几个问题，这些问题考虑了所寻求的信号和系统上的一组先验。例如，盲源分离通常是通过单瞄准的配方来解决的，该公式依赖于与特定信号的给定特性相关的分离标准（来源）。但是，在许多实际情况下，有多个财产要被利用，因此，可以使用一组分离标准来恢复原始信号。在这种情况下，本文通过基于多目标优化的方法解决了分离问题。与现有方法仅提供原始信号的一个估计值不同，我们的建议会导致系统用户可以利用的一系列解决方案来做他/她的决定。通过数值实验在一组生物医学信号上获得的结果突出了所提出的方法的生存能力，该方法与通过单体目标公式相比，该方法更接近平均平方误差溶液。此外，由于我们的建议非常笼统，因此这项工作也有助于鼓励未来的研究开发专家系统，以利用不同来源分离问题中的多目标配方。
Several problems in signal processing are addressed by expert systems which take into account a set of priors on the sought signals and systems. For instance, blind source separation is often tackled by means of a mono-objective formulation which relies on a separation criterion associated with a given property of the sought signals (sources). However, in many practical situations, there are more than one property to be exploited and, as a consequence, a set of separation criteria may be used to recover the original signals. In this context, this paper addresses the separation problem by means of an approach based on multi-objective optimization. Differently from the existing methods, which provide only one estimate for the original signals, our proposal leads to a set of solutions that can be utilized by the system user to take his/her decision. Results obtained through numerical experiments over a set of biomedical signals highlight the viability of the proposed approach, which provides estimations closer to the mean squared error solutions compared to the ones achieved via a mono-objective formulation. Moreover, since our proposal is quite general, this work also contributes to encourage future researches to develop expert systems that exploit the multi-objective formulation in different source separation problems.
