计算技术与自动化2024，Vol.43Issue(2)：70-76,7.DOI:10.16339/j.cnki.jsjsyzdh.202402012
多传感器的BPNN和SVM多源异构数据融合算法
Multi Sensor Heterogeneous Data Fusion Algorithm Based on BPNN and SVM
摘要
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 fea-ture vector;Based on the support vector machine's strong generalization ability and convex optimization,the optimal classi-fication 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.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Key words
BP neural network/support vector machine/multi source heterogeneous data/data cleaning/data fusion分类
信息技术与安全科学引用本文复制引用
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,2024,43(2):70-76,7.基金项目
国网新一代人工智能科技项目(2020AAA0103400) （2020AAA0103400）
