Abstract:

When using data from a single data source to complete tasks, there may be significant errors in the data, and there may even be data missing, which can affect the progress of the task. A dual channel data association and fusion algorithm based on fuzzy mathematics theory is proposed for this purpose. The correlation of dual channel data is measured and the missing data in the dual channel data is predicted according to the missing data prediction process. The missing data in the dual channel dataset is filled in to obtain complete dual channel data. The dual channel data is standardized, and the principal component analysis is used to calculate

the similarity between the dual channel data and the principal components, obtaining the comprehensive support level of the dataset, and obtain effective data. By using fuzzy mathematics theory, effective data is fuzzified, and the closeness between the fuzzification results and real data is calculated to determine the data fusion weight, in order to achieve dual channel data association and fusion. The experimental results show that using the proposed algorithm for dual channel data association fusion, when the total number of data reaches 1 500, the value of the comprehensive evaluation index exceeds 9, indicating that the proposed algorithm can improve the accuracy of dual channel data association fusion and has good dual channel data association fusion results.

Key words: fuzzy mathematics theory, dual channel data, association fusion algorithm, standardized processing, missing data filling