文献类型：期刊文献

英文题名：Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization

作者：Liu, Shuangyin[1,2];Xu, Longqin[2];Li, Daoliang[1];Li, Qiucheng[1];Jiang, Yu[1];Tai, Haijiang[1];Zeng, Lihua[1,3]

机构：[1]China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China;[2]Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Guangdong, Peoples R China;[3]Agr Univ Hebei, Coll Mech & Elect Engn, Baoding 071001, Peoples R China

年份：2013

卷号：95

起止页码：82

外文期刊名：COMPUTERS AND ELECTRONICS IN AGRICULTURE

收录：SCI-EXPANDED(收录号：WOS:000320638800009)、、EI(收录号：20132116357214)、Scopus(收录号：2-s2.0-84877815112)、WOS

基金：The authors would like to thank native English speaking expert Schiller Laurie Elaine, to polish our paper. Finally, this paper was supported by the National Science and Technology Supporting Plan Project 2011BAD21801-1, Guangdong Science and Technology Plan Project 2012A020200008 and 2012B091100431, and National Natural Science Foundation in the framework of Project 61100115, Guangdong Natural Science Foundation of project S2012010008261.

语种：英文

外文关键词：Least squares support vector regression; Improved particle swarm optimization; algorithm; Dissolved oxygen content prediction

外文摘要：It is important to set up a precise predictive model to obtain clear knowledge of the prospective changing conditions of dissolved oxygen content in intensive aquaculture ponds and to reduce the financial losses of aquaculture. This paper presents a hybrid dissolved oxygen content prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by improved particle swarm optimization (IPSO) algorithm. In view of the slow convergence of particle swarm algorithm (PSO), improved PSO with the dynamically adjusted inertia weight was based on the fitness function value to improve convergence. Then a global optimizer, IPSO, was employed to optimize the hyperparameters needed in the LSSVR model. We adopted an IPSO-LSSVR algorithm to construct a non-linear prediction model. IPSO-LSSVR was tested and compared to other algorithms by applying it to predict dissolved oxygen content in river crab culture ponds. Experiment results show that the proposed model of IPSO-LSSVR could increase the prediction accuracy and execute generalization performance better than the standard support vector regression (SVR) and BP neural network, and it is a suitable and effective method for predicting dissolved oxygen content in intensive aquaculture. (C) 2013 Elsevier B.V. All rights reserved.