计算机与数字工程Issue(10)：1853-1856,1893,5.DOI:10.3969/j.issn1672-9722.2014.10.024
基于量子粒子群神经网络的太阳黑子数预测磁
Sunspot Number Prediction Based on Quantum-behaved Particle Swarm-based Neural Network
关学忠 1皇甫旭 1李欣 2佟宇 1孙立刚1
作者信息
- 1. 东北石油大学电气信息工程学院 大庆 163318
- 2. 东北石油大学计算机与信息技术学院 大庆 163318
- 折叠
摘要
Abstract
In order to enhance the prediction accuracy of sunspot numbers ,a novel prediction model based on quantum-behaved particle swarm-based neural networks was proposed .First ,taking the sunspot annual averages of first 18 Solar cycle (1755 ~ 1953) as the training set ,the weights and threshold values of BP neural networks were adjusted by quantum-behaved particle swarm optimization (QPSO) ,and then the training process was completed .Secondly ,the sunspot annual averages of the 19th Solar cycle (1954 ~ 2013) were employed to verify the prediction ability of the proposed model .Experimental results showed the proposed model was obviously superior to the general BP neural networks in both approximation ability and pre -diction accuracy ,which revealed the training based on QPSO had a certain potential in enhancing the prediction ability of the general BP neural networks .关键词
太阳黑子年均值/量子粒子群优化/BP 神经网络/时间序列预测Key words
sunspots annual average/QPSO/BP neural networks/time series prediction分类
信息技术与安全科学引用本文复制引用
关学忠,皇甫旭,李欣,佟宇,孙立刚..基于量子粒子群神经网络的太阳黑子数预测磁[J].计算机与数字工程,2014,(10):1853-1856,1893,5.
