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Bayesian-Wavelet-Based Multisource Decision Fusion
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-07-05 , DOI: 10.1109/tim.2021.3094829
Wangzhuo Yang , Bo Chen , Li Yu

Multisource information perception is the basic means for humans to explore the universe, and information decision fusion has become a crucial technique in some fields. Limited by the unknown distribution of multisource information, this article proposes a decision fusion method based on the distributed wavelet neural network (DWNN) and the Bayesian inference. The proposed fusion decision framework is a parallel network that consists of an empirical wavelet filtering layer, feature extraction layer, local decision layer, and decision fusion layer. Notice that the activation function of the feature extraction layer is a wavelet, and this nonlinear operation can be considered as a wavelet transform of the multisource data. Subsequently, an iterative learning method is adopted to minimize the estimated loss of the subnetwork and approximate the optimum decision model for local data. Furthermore, a decision fusion rule with the minimum Bayesian-like cost based on local evidence is adopted in the fusion center. Finally, a typical multisource decision fusion experiment of human surface electromyography (sEMG) and time series classification is presented to show the effectiveness of the proposed decision fusion structure.

中文翻译:

基于贝叶斯小波的多源决策融合

多源信息感知是人类探索宇宙的基本手段,信息决策融合已成为某些领域的关键技术。受多源信息分布未知的限制,本文提出了一种基于分布式小波神经网络(DWNN)和贝叶斯推理的决策融合方法。所提出的融合决策框架是一个并行网络,由经验小波滤波层、特征提取层、局部决策层和决策融合层组成。注意特征提取层的激活函数是一个小波,这个非线性操作可以看作是多源数据的小波变换。随后,采用迭代学习的方法最小化子网的估计损失,逼近局部数据的最优决策模型。此外,融合中心采用基于局部证据的具有最小类贝叶斯成本的决策融合规则。最后,提出了人体表面肌电图(sEMG)和时间序列分类的典型多源决策融合实验,以证明所提出的决策融合结构的有效性。
更新日期:2021-07-30
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