Runoff forecast based on extreme learning machine (ELM) optimized by virus evolutionary genetic algorithm

被引：0
|
作者：
Changming, Cheng [1 ]
机构：
[1] Department of water conservancy, Chongqing Water Resources and Electric Engineering College, China
来源：
International Journal of Earth Sciences and Engineering | 2014年 / 7卷 / 05期
关键词：
Genetic algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper proposes the runoff forecast method based on improved genetic Extreme Learning Machine (ELM). ELM algorithm randomly sets input weight and errors, and thus requires a large number of hidden layer nodes to achieve the expected precision. This paper adopts Virus Evolutionary Genetic Algorithm (VEGA) to select the optimum input weight matrix and errors in the hidden layers so as to calculate the output weight matrix. After testing, the runoff forecast model based on ELM optimized by improved genetic algorithm has high precision of prediction. It is an effective method to do runoff forecast. ©2014 CAFET-INNOVA TECHNICAL SOCIETY
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页码：1690 / 1695
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