典型文献
Predicting solutions of the Lotka-Volterra equation using hybrid deep network
文献摘要：
Prediction of Lotka-Volterra equations has always been a complex problem due to their dynamic prop-erties. In this paper, we present an algorithm for predicting the Lotka-Volterra equation and investigate the prediction for both the original system and the system driven by noise. This demonstrates that deep learning can be applied in dynamics of population. This is the first study that uses deep learning al-gorithms to predict Lotka-Volterra equations. Several numerical examples are presented to illustrate the performances of the proposed algorithm, including Predator nonlinear breeding and prey competition systems, one prey and two predator competition systems, and their respective systems. All the results suggest that the proposed algorithm is feasible and effective for predicting Lotka-Volterra equations. Fur-thermore, the influence of the optimizer on the algorithm is discussed in detail. These results indicate that the performance of the machine learning technique can be improved by constructing the neural networks appropriately.
文献关键词：
中图分类号：
作者姓名：
Zi-Fei Lin;Yan-Ming Liang;Jia-Li Zhao;Jiao-Rui Li
作者机构：
School of Statistics,Xi'an University of Finance and Economics,Xi'an 710100,China;China(Xi'an)Institute for Silk Road Research,Xi'an 710100,China
文献出处：
引用格式：
[1]Zi-Fei Lin;Yan-Ming Liang;Jia-Li Zhao;Jiao-Rui Li-.Predicting solutions of the Lotka-Volterra equation using hybrid deep network)[J].力学快报（英文）,2022(06):423-431
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B类：
Predicting,solutions,Lotka,Volterra,using,hybrid,deep,Prediction,equations,has,always,been,complex,problem,due,their,erties,In,this,paper,we,algorithm,predicting,investigate,prediction,both,original,driven,by,noise,This,demonstrates,that,learning,can,applied,dynamics,population,first,study,uses,gorithms,Several,numerical,examples,are,presented,illustrate,performances,proposed,including,Predator,nonlinear,breeding,prey,competition,systems,one,predator,respective,All,results,suggest,feasible,effective,Fur,thermore,influence,optimizer,discussed,detail,These,indicate,machine,technique,improved,constructing,neural,networks,appropriately
AB值：
0.560874
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