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作 者:葛枫晔 郑晓伟[2,3] GE Fengye;ZHENG Xiaowei(School of Navigation and Naval Architecture,Dalian University of Ocean,Dalian 116023,Liaoning,China;Fishery Machinery and Instrument Research Institute,Chinese Academy of Fishery Sciences,Shanghai 200092,China;National R&D Branch Center For Aquatic Product Processing Equipment,Shanghai 200092,China)
机构地区:[1]大连海洋大学航海与船舶工程学院,辽宁大连116023 [2]中国水产科学研究院渔业机械仪器研究所,上海200092 [3]国家水产品加工装备研发分中心(上海),上海200092
出 处:《渔业现代化》2025年第1期119-128,共10页Fishery Modernization
基 金:中国水产科学研究院基本科研业务费“水产品加工装备项目(2023TD75)”。
摘 要:为解决称重传感器在船载状态下稳定性和称重精度差的问题,研究了船舶摇摆对四臂桥电阻式称重传感器输出的影响。获取单体鱼在横倾角度(0°~22.5°)和纵倾角度(0°~10°)的质量数据,对称重传感器输出质量和倾斜角度进行多元回归分析。构建多层级BP神经网络架构,架构为4层前向反馈[3-8-1-1]BP神经网络,使用BP神经网络进行预测并用训练模型来调整参数,使其能准确预测鱼的质量,并进行算法补偿方法研究。结果显示:不同原料质量下,最优线性方程中所估测的多元回归系数均达到显著水平(P<0.01),表明所建立的线性回归方程具有较高的可靠性和良好的线性度,回归分析和有限元分析的结果一致。利用所建立的BP神经网络模型构建船载瞬时称重数据补偿方法,通过船舶横纵倾斜角度来预测实际质量值,BP模型对横倾、纵倾单一作用下,以及复合作用下称重数据的变化均表现出较好的有效性、高精度和较好的泛化能力,BP模型补偿后误差率降低至0.092%,非常接近水平状态下的质量值,误差率低。该研究成果可为船载条件下水产品称重提供参考。In order to solve the problem of poor stability and weighing accuracy of the load cell under ship load condition,the effect of ship swaying on the output of four-arm bridge resistive load cell was studied.The mass data of single fish at transverse tilt angle(0°-22.5°)and longitudinal tilt angle(0°-10°)were obtained,and analyzed by multivariate regression analysis on the output mass of the load cell and the tilt angle.A multi-layer BP neural network structure was constructed with the architecture of a 4-layer forward feedback[3-8-1-1]BP neural network,and the BP neural network was used to make predictions and adjust the parameters with a training model so that it could accurately predict the quality of the fish,and the algorithmic compensation method was studied.The results showed that the multiple regression coefficients estimated in the optimal linear equations reached the significant level(P<0.01)for different raw material qualities,indicating that the established linear regression equations had high reliability and good linearity,and the results of regression analysis and finite element analysis were consistent.The BP neural network model was used to construct the compensation method of shipboard instantaneous weighing data,and the actual mass value was predicted by the ship's transverse and longitudinal tilt angle,and the BP model showed good validity,high accuracy and good generalization ability for the change of weighing data under single action of transverse tilt and longitudinal tilt,as well as under the composite action,and the error rate of the BP model was reduced to 0.092%after compensation,which is very close to that of the mass value under the horizontal state,with a low error rate.The results of this research can provide a reference for the weighing of aquatic products under shipboard conditions.
分 类 号:S985.9[农业科学—捕捞与储运]
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