摘要
结合印度洋大眼金枪鱼渔业应用背景,提出一种新的面向渔情预测的智能数据处理模型。首先利用支持向量机(SVM)对样本数据进行训练,并运用模糊分类器对支持向量进行规则提取;然后对条件和结论分别进行可拓变换和传导变换,获得渔场动态知识。实验结果表明,获得的动态知识具有更强的解释性,对丰富渔情预测方法、解释渔场形成机制具有重要意义。
This paper proposed a novel intelligent data processing model which applied to fishery forecasting based on the Indian Ocean bigeye tuna fishery application background. Firstly, obtained the support vectors by training the SVM with the sample data. Secondly, extracted the rules from the support vectors by the fuzzy classifier method. Lastly obtained the fishery dynamic knowledge by the extension transformation for the conditions and the conduction transformation for the conclusions. Experimental results show that the dynamic knowledge is of a forceful interpretive capacity, and it enriches fisheries forecasting methods and reveals the formation mechanism of fishing grounds.
出处
《计算机应用研究》
CSCD
北大核心
2009年第12期4520-4522,共3页
Application Research of Computers
基金
上海市教育委员会资助项目(06KZ016
09YZ267)
关键词
支持向量机
模糊集
规则提取
可拓数据挖掘
印度洋大眼金枪鱼
support vector machines（SVM）
fuzzy set
rule extraction
extension data mining
Indian Ocean bigeye tuna
