基于贝叶斯网的象山港网箱轮养周期预测模型

Study on the net-cages mobile cycle prediction model based on Bayesian network in the Xiangshan Bay

贝叶斯网络具有强大的推理能力,能与先验知识和数据结合,进行定性和定量分析,提供了一条有效的处理预测问题的途径,首先介绍了以上贝叶斯网络及其特点,并讨论如何学习贝叶斯网络结构,然后由专家知识和给定数据,构造了一个海底网箱养殖的贝叶斯网络预测模,该模型能有效的表达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移动周期进行预测和决策。实验结果表明,试验数据显示评价的准确性是89.7%。以上证明该方法是有效可行的,表明贝叶斯网络是一种很有前途的预测评价方法。

Bayesian network has a powerful ability for reasoning and semantic representation, which combines quantitative analysis with prior knowledge and observed data, and provides an effective way to deal with prediction. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to learn a Bayesian network structure from given data, and then constructed a model of the Bayesian network for the period of Xiangshan Bay cage culture with expert knowledge and the dataset. The method can be effective expression cage culture environment between the various indicators of a causal relationship and designated by the cage culture of mobile cycle forecasting and decision-making. The experimental results based on the test dataset show that evaluation accuracy is 89.7%. All these prove the method is feasible and efficient and indicate that Bayesian network is a promising approach for the prediction.