中文摘要：

水产养殖池塘是一个多变量、非线性和大时延系统，其中溶解氧的预测也是一个复杂的问题。针对大连某水产养殖池塘，作者建立了一个基于Levenberg—Marquardt（LM）神经网络和遗传算法（GA）的溶解氧预测模型GA—LM，并将该模型与传统的BP神经网络进行比较分析。结果表明：使用本研究中建立的GA-LM模型预测的溶解氧值和实际测定值吻合较好，预测更为精准，运行时间明显减少。

英文摘要：

The prediction of dissolved oxygen （DO） level is complicated in aquaculture ponds as a complex system with multi-variables, nonlinearity and long-time lag. In this study, GA-LM, a hybrid neural network model com- bining Levenberg Marquardt（LM） algorithm and Genetic Algorithm （GA） was developed for DO level predicting in an aquaculture pond at Dalian, China. The The comparison of performance of GA-LM with the conventional Back -Propagation （BP） algorithm revealed that the predicted DO values using GA-LM model are in good agreement with the measured data, indicating that the model is capable of predicting DO accurately and rapidly.