基于EEMD和萤火虫算法优化SVM的溶解氧预测

养殖池塘中的溶解氧(DO)对水产品的生长和品质有着至关重要的作用.为了提高溶解氧预测的准确性和有效性,提出了一种基于集合经验模态分解(EEMD)和萤火虫算法(FA)优化支持向量机(SVM)的组合预测模型.首先,将DO 时间序列通过集合经验模态分解为一组去除噪声的并相对稳定的子序列.接着,利用相空间重构(PSR)重建分解...

Prediction of Dissolved Oxygen in Aquaculture Based on EEMD and SVM Optimized by the Firefly Algorithm

The dissolved oxygen(DO)in the pond plays an important role in the growth and quality of aquatic products.In order to improve the prediction accuracy and effectiveness of DO,a combined prediction model based on ensemble empirical mode decomposition(EEMD)and firefly algorithm(FA)optimized support vector machine(SVM)is proposed.Firstly,the DO time series were decomposed into a group of relatively stable subsequence by ensemble empirical mode decomposition.Secondly,the decomposed subsequence was reconstructed by the phase space reconstruction(PSR),and the subsequence was modeled and predicted using the SVM in phase space.Finally,the parameters of SVM were optimized by using firefly algorithm to obtain the predicted value of original DO sequence.This paper used single point iterative method to achieve multi-step prediction,in order to obtain the forecasting result of 24 hours in the future.The simulation results demonstrate that the proposed combinatorial prediction model EEMD-FA-SVM has better prediction effect than FA-SVM,EEMD-FA-BP and EEMD-PSO-SVM,which is proven to be an effective way to predict DO,and it can meet the actual demand for fine management of water quality in modern fishery culture.