Neural data fusion algorithms based on a linearly constrained least square method
Neural data fusion algorithms based on a linearly constrained least square method
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DOI：
10.1109/72.991418
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发表时间：
2002-03
影响因子：
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通讯作者：
Youshen Xia;H. Leung;É. Bossé
中科院分区：
文献类型：
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作者：
Youshen Xia;H. Leung;É. Bossé
提出了两种基于线性约束最小二乘（LCLS）方法的神经数据融合新算法。LCLS方法用于最小化线性融合信息的能量，两个神经网络算法被开发来克服LCLS方法中出现的样本协方差矩阵的病态和奇异问题。提出的神经融合算法是使用软件和硬件实现的样本。与现有的融合方法相比，所提出的神经数据融合方法具有无偏的统计特性，并且不需要任何关于噪声协方差的先验知识。结果表明，当样本协方差矩阵奇异时，所提出的神经融合算法全局收敛到最优融合解;当样本协方差矩阵非奇异时，所提出的神经融合算法全局指数收敛.我们提出的神经融合方法的图像和信号融合，它表明，该解决方案的质量可以大大提高所提出的技术。
Two novel neural data fusion algorithms based on a linearly constrained least square (LCLS) method are proposed. While the LCLS method is used to minimize the energy of the linearly fused information, two neural-network algorithms are developed to overcome the ill-conditioned and singular problems of the sample covariance matrix arisen in the LCLS method. The proposed neural fusion algorithms are samples for implementation using both software and hardware. Compared with the existing fusion methods, the proposed neural data fusion method has an unbiased statistical property and does not require any a priori knowledge about the noise covariance. It is shown that the proposed neural fusion algorithms converge globally to the optimal fusion solution when the sample covariance matrix is singular, and converge globally with exponential rate when the sample covariance matrix is nonsingular. We apply the proposed neural fusion method to image and signal fusion, and it is shown that the quality of the solution can be greatly enhanced by the proposed technique.