{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:46:17Z","timestamp":1776811577885,"version":"3.51.2"},"reference-count":22,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,1,26]]},"abstract":"<jats:p>Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.<\/jats:p>","DOI":"10.3233\/jcm-215729","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T10:31:42Z","timestamp":1640341902000},"page":"253-263","source":"Crossref","is-referenced-by-count":0,"title":["Neural network-based optimization model for sprint data collection"],"prefix":"10.66113","volume":"22","author":[{"given":"Guangfei","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"15","key":"10.3233\/JCM-215729_ref1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.engstruct.2017.11.005","article-title":"Metamodel-based design optimization of structural one-way slabs based on deep learning neural networks to reduce environmental 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