基于免疫进化算法的贝叶斯网络预测网箱转移周期

Predicting the shift cycle of the net-cage by the Bayesian network based on immune evolutionary algorithms

中文关键词: 转移周期  网箱养殖  贝叶斯网络  免疫进化算法  增量学习

英文关键词: Shift cycle  Aquaculture  Bayesian network  Immune evolutionary algorithm  Incremental learning

中文摘要:

      以象山港网箱养殖区2000~2006年的监测数据作为训练数据，结合专家知识采用基于免疫进化的贝叶斯网络结构增量式学习算法，构建了海底网箱转移的贝叶斯网络预测模型。该模型能有效的揭示出网箱养殖环境各个指标之间的因果关系，进而可以对指定的网箱养殖的网箱转移周期进行预测和决策。结果表明，评价的准确性是91.7%，证明该方法是有效可行的。

英文摘要:

      By taking the monitoring data of Xiangshan Bay from the year of 2000 to 2006 as the training data and referring to the prior knowledge, a Bayesian network was constructed through the incremental learning based on the immune heredity algorithm. The model can effectively express the causal relationship among the various indicators in the net-cage aquaculture environment, and the shift cycle of the net-cage aquaculture at Xiangshan can be predicted. The result showed that the appraisal accuracy reached 91.7%, which meant that this method is feasible.