基于神经网络的南方夏季蛋鸡舍环境与产蛋性能关系模型研究  

Research on the relational model between layer house environment and egg production performance in southern summer based on neural network

作　　者：谢苗苗[1] 李华龙 XIE Miaomiao;LI Hualong(Jianghuai College,Anhui University,Hefei 230031,China;Institute of Intelligent Machines,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China)

机构地区：[1]安徽大学江淮学院,合肥230031 [2]中国科学院合肥物质科学研究院智能机械研究所,合肥230031

出　　处：《黑龙江畜牧兽医》2024年第9期41-47,共7页Heilongjiang Animal Science And veterinary Medicine

基　　金：国家自然科学基金项目(31902205);安徽省高等学校自然科学研究重点项目(KJ2021A1218)。

摘　　要：为了解决南方地区夏季高温高湿蛋鸡舍环境质量难以精准评价与调控的问题,试验构建了基于神经网络的南方夏季蛋鸡舍环境与产蛋性能关系模型,将采集的蛋鸡舍多环境因子数据作为输入,将依据产蛋率划分的环境质量等级作为输出,对反向传播(BP)神经网络、径向基(RBF)神经网络和广义回归(GRNN)神经网络三种神经网络进行训练验证,得到蛋鸡舍环境质量初步评价结果,然后将归一化后神经网络组的输出作为改进D-S证据理论基本概率分配的输入,利用改进的D-S证据理论方法,融合计算得到蛋鸡舍环境质量的综合评价结果。结果表明:在神经网络初级融合评价有冲突的情况下,改进的D-S证据理论法对蛋鸡舍环境状态为差的支持率为0.796 4,而传统D-S证据理论方法、Murphy方法和邓勇方法对蛋鸡舍环境状态为差的支持率分别为0.698 7,0.687 8,0.694 0。说明试验构建的综合评价模型对蛋鸡舍环境的融合评价精度和准确率更高,可以有效解决融合决策失误问题,可用于南方夏季高温高湿蛋鸡舍环境的精准调控。In order to solve the problems of difficult accurate evaluation and regulation of environmental quality of high-temperature and high humidity layer house in southern region in summer,the relational model between layer house environment and egg performance was constructed based on multiple neural networks.The collected environmental factor data of layer house were used as input,and the environmental quality grade divided according to egg production rate were used as output;three neural networks BP,RBF,and GRNN,were trained and validated.The preliminary judgment result of the environmental quality of the layer house was obtained.Then,the output data of the normalized neural network group is taken as the inputs of the basic probability distribution of the improved D-S evidence theory.Finally the comprehensive evaluation result of environmental quality of the layer house was obtained by the improved D-S evidence theory and the fusion judgment.The results showed that in the case of conflicting neural network primary fusion decisions,the improved D-S algorithm had support rates of 0.796 4 for poor environmental status in the layer house,while the classical D-S method,Murphy method,and Deng Yong method had support rates of 0.698 7,0.687 8,and 0.694 0 respectively.The results indicated the comprehensive evaluation model constructed by the experiment had higher fusion judgment precision and accuracy for the laying house environment,and could effectively solve the fusion decision error problem,which provided a reference for the accurate regulation of high temperature and high humidity layer house environment in southern summer.

关 键 词：多神经网络 改进的D-S证据理论 蛋鸡舍 高温高湿环境 环境评价 融合决策 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]