外文摘要：To address the poor fitness and low accuracy of multiobjective parameter optimization, the BP neural network-based constrained multiobjective optimization method was applied to optimize a seed-metering device. Taking the 2BQ-15 type Panax notoginseng seedmetering device as the research object, the picking hole column diameter, forward velocity, and dropping seed point-to-picking hole roll distance were selected as the experimental factors, and the quality index, missing index and multiple index were selected as the performance indicators. The experimental scheme was designed by the quadratic orthogonal rotation combination, and the BP neural network of the precision seed-metering device was built from the experimental data. The seed-metering device was optimized by the proposed method, and the optimal parameter combinations were obtained as follows: the picking hole column diameter was 27 mm, the forward velocity was 0.50 m/s, and the dropping seed point-to-picking hole roll distance was 330 mm. Under such parameter combinations, the quality index is 93.4%, the missing index is 3.15%, and the multiple index is 3.35%. Finally, a verification test was carried out on the basis of the optimization results, the errors were within the allowable range, and the test results and optimized results were consistent.
外文关键词：seed-metering device;BP neural network;Panax notoginseng;drilling device;optimization design
作者：Dong, Zhigui;Jiang, Jiahuai;Wang, Yanchao
作者单位：Liaoning Inst Sci & Technol
期刊名称：ENGENHARIA AGRICOLA
期刊影响因子：0.0
出版年份：2024
出版刊次：44
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台