Blind source separation based on self-organizing neural network
Blind source separation based on self-organizing neural network
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DOI：
10.1016/j.engappai.2005.09.006
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发表时间：
2006-04-01
影响因子：
8
通讯作者：
Foo, S
中科院分区：
文献类型：
--
作者：
Meyer-Bäse, A;Gruber, P;Foo, S
这篇文章描述了一种自组织的神经网络，它可以从典型的传感器信号中恢复潜在的原始源。除了源信号在统计上独立且非平稳的事实外，不需要关于源的统计属性和线性变换的系数的特定信息。对于现实生活中的应用程序来说，这通常是正确的。我们提出了一种使用神经网络的在线学习方案，并利用信源的非平稳性来实现分离。网络参数的学习规则是由依赖时间的代价函数的最陡下降最小化得到的，该代价函数仅在网络输出彼此不相关时才取最小值。在这个过程中，将问题分解为两个学习问题，其中一个通过反Hebbian学习来解决，另一个通过Hebbian学习过程来解决。我们还将我们的算法的性能与该任务的其他解决方案进行了比较。(C)2005爱思唯尔有限公司。保留所有权利。
This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. This is often true for real life applications. We propose an online learning solution using a neural network and use the nonstationarity of the sources to achieve the separation. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other. In this process divide the problem into two learning problems one of which is solved by an anti-Hebbian learning and the other by an Hebbian learning process. We also compare the performance of our algorithm with other solutions to this task. (C) 2005 Elsevier Ltd. All rights reserved.