多传感器的BPNN和SVM多源异构数据融合算法
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引用本文:王晓琪,陈颖聪,谢敏敏,张嘉慧,蔡上.多传感器的BPNN和SVM多源异构数据融合算法[J].计算技术与自动化,2024,(2):70-76
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王晓琪,陈颖聪,谢敏敏,张嘉慧,蔡上 (广东电网有限责任公司 梅州供电局广东 梅州 514021) 
中文摘要:多传感器的多源异构数据融合处理时,大量的冗余数据及复杂的非线性可分空间导致能耗较大,为此,提出了BP神经网络和支持向量机的多源异构数据融合算法。以数据关系构建约束条件,利用BP神经网络算法建立数据清洗模型,判定节点变量的活跃程度,优化数据输入;建立数据集合,提取数据特征向量;利用支持向量机泛化能力强、凸优化的特点,获取特征的最优分类超平面,获得非线性可分多源数据集转化为高维线性可分空间的最优决策值,输出结果。实验结果表明,该算法融合多源异构数据的能量消耗小、延迟低,融合效果好。
中文关键词:BP神经网络  支持向量机  多源异构数据  数据清洗  数据融合
 
Multi Sensor Heterogeneous Data Fusion Algorithm Based on BPNN and SVM
Abstract:In the process of multi-sensor multi-source heterogeneous data fusion processing, a large number of redundant data and complex nonlinear separable space lead to high energy consumption. Therefore, a multi-source heterogeneous data fusion algorithm based on BP neural network and support vector machine is proposed. Based on the data relationship, the constraint conditions were established, and the BP neural network algorithm was used to establish the data cleaning model, and the activity degree of node variables was determined to optimize the data input. To set up data set and extract data feature vector; Based on the support vector machine’s strong generalization ability and convex optimization, the optimal classification hyperplane of the features is obtained, and the optimal decision value of the nonlinear separable multi-source data set is obtained into the high-dimensional linear separable space. Experimental results show that this algorithm has low energy consumption, low delay and good fusion effect.
keywords:BP neural network  support vector machine  multi source heterogeneous data  data cleaning  data fusion
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