基于DBN-LSSVR的南美白对虾养殖溶解氧预测  

Prediction of dissolved oxygen in Litopenaeus vannamei culture based on deep belief network and least squares support vector regression

作　　者：徐龙琴 刘双印[1,2] 张垒 覃庆伟[1,2] 贺超波 郑建华[1,2] 沈玉利 XU Ixmgqin;LIU Shuangyin;ZHANG Lei;QIN Qingwei;HE Chaobo;ZHENG Jianhua;SHEN Yuli(College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;Guangdong Province Key Laboratory of Waterfowl Healthy Breeding, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

机构地区：[1]仲恺农业工程学院信息科学与技术学院,广东广州510225 [2]仲恺农业工程学院广东省水禽健康养殖重点实验室,广东广州510225

出　　处：《仲恺农业工程学院学报》2017年第4期1-7,共7页Journal of Zhongkai University of Agriculture and Engineering

基　　金：国家自然科学基金(61471133);广东省科技计划(2017A070712019;2016A040402043;2016A030303058);广东省普通高校省级重大科研项目(2016KZDXM001);广东省教育厅育苗工程(KKA13412R)资助

摘　　要：为了提高南美白对虾(Litopenaeus vannamei)养殖溶解氧预测的精度,提出了深度信念网络融合最小二乘支持向量回归机(Deep belief nets-least squares support vector regression,DBN-LSSVR)的南美白对虾养殖溶解氧预测模型.首先,采用深度信念网络(Deep belief nets,DBN)方法,多尺度提取养殖水质时序数据的特征向量;然后,使用提取的养殖水质特征向量训练和优化DBN-LSSVR,构建了基于DBN-LSSVR的对虾养殖水质溶解氧预测模型;最后,以广州市番禺区南美白对虾养殖水质溶解氧实测数据为基础,对预测模型进行了实验验证,并与浅层BP神经网络、标准最小二乘支持向量回归机进行了对比分析.所构建的模型具有较高的预测精度和泛化性能,是一种有效的南美白对虾养殖溶解氧预测方法.In order to improve the prediction accuracy of the dissolved oxygen in Litopenaeus vannamei aquaculture ponds, a dissolved oxygen prediction model based on deep belief network （DBN） model and least squares support vector regression （LSSVR） was proposed. First, the DBNs were employed using ex- traction feature vectors of time series water quality data. Then, the feature vector as a training and test set for DBN-LSSVR model training and optimization. The combinations of the best parameters were obtained automatically after the optimization, which construct the nonlinear prediction model between the dissolved oxygen and the impact factors. Finally, validation and comparative analysis of the model were carried out using the measured data in Panyu district, Guangzhou. The proposed prediction model of DBN-LSSVR had high prediction accuracy and generalization ability, and it was a suitable and effective method for predicting dissolved oxygen in intensive density Litopenaeus vannamei culture.

关 键 词：溶解氧预测 深度信念网络 最小二乘支持向量回归机 南美白对虾养殖 特征提取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]