文献类型：期刊文献

英文题名：A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture

作者：Liu, Shuangyin[1,2,3,4];Xu, Longqin[1];Jiang, Yu[2,3,4];Li, Daoliang[2,3,4];Chen, Yingyi[2,3,4];Li, Zhenbo[2,3,4]

机构：[1]Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Guangdong, Peoples R China;[2]China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China;[3]China Agr Univ, Beijing ERC Internet Things Agr, Beijing 100083, Peoples R China;[4]China Agr Univ, Beijing ERC Adv Sensor Technol Agr, Beijing 100083, Peoples R China

年份：2014

卷号：29

起止页码：114

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:000332811300010)、、EI(收录号：20140817360917)、Scopus(收录号：2-s2.0-84894086399)、WOS

基金：The authors would like to thank Dr Van Willigenburg of Wageningen University and American journal experts polishing our paper. Finally, this paper was supported by the National Science and Technology Supporting Plan Project 2011BAD21B01-1, Guangdong Science and Technology Plan Project 2012A020200008, 2012B09050 0008 and 2012B091100431, and the National Natural Science Foundation in the framework of Project 61100115, Guangdong Natural Science Foundation of project S2013010014629 and S2012010008261.

语种：英文

外文关键词：Least squares support vector regression; Wavelet analysis; Cauchy particle swarm optimization algorithm; Dissolved oxygen content forecasting; Parameter optimization

外文摘要：To increase prediction accuracy, reduce aquaculture risks and optimize water quality management in intensive aquaculture ponds, this paper proposes a hybrid dissolved oxygen content forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several resolution frequency signal subsets using the wavelet analysis method. Independent prediction models were developed using decomposed signals with wavelet analysis and least squares support vector regression. The independent prediction values were reconstructed to obtain the ultimate prediction results. In addition, because the kernel parameter a and the regularization parameter gamma in the LSSVR training procedure significantly influence forecasting accuracy, the Cauchy particle swarm optimization (CPSO) algorithm was used to select optimum parameter combinations for LSSVR. The proposed hybrid model was applied to predict dissolved oxygen in river crab culture ponds. Compared with traditional models, the test results of the hybrid WA-CPSO-LSSVR model demonstrate that de-noising and capturing non-stationary characteristics of dissolved oxygen signals after WA comprise a very powerful and reliable method for predicting dissolved oxygen content in intensive aquaculture accurately and quickly. (C) 2013 Elsevier Ltd. All rights reserved.