基于时变阈值过程神经网络的太阳黑子数预测  

Sunspot number prediction based on process neural network with time-varying threshold functions

作　　者：丁刚[1] 钟诗胜[1] 

机构地区：[1]哈尔滨工业大学机电工程学院,哈尔滨150001

出　　处：《物理学报》2007年第2期1224-1230,共7页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:60373102;60572174)资助的课题~~

摘　　要：太阳黑子活动直接影响着外层空间环境的变化,为保证航天飞行任务的安全必须对其进行有效预测.为此,提出了一种基于时变阈值过程神经网络的时间序列预测模型.为简化模型的计算复杂度,开发了一种基于正交基函数展开的学习算法.文中分析了模型的泛函逼近能力,并以Mackey-Glass时间序列预测为例验证了所提模型及其学习算法的有效性.最后,将该预测模型用于太阳活动第23周太阳黑子数平滑月均值预测,取得了满意的结果,应用结果同时表明:所提预测方法与其他传统预测方法相比预测精度有所提高,具有一定的理论和实用价值.The activity of the sunspot influences the space environment directly. In order to guarantee the flight safety of the spacecraft in the space, it is necessary to predict the sunspot number effectively. To solve this problem, a time series prediction model based on the process neural network with time-varying threshold functions is proposed. To simplify the calculation, a learning algorithm based on the expansion of the orthogonal basis functions is developed. The functional approximation capability of the proposed prediction model is analyzed, and the effectiveness of the prediction model and its learning algorithm is validated by the prediction of the Mackey-Glass time series. Finally, the proposed time series prediction model is utilized to predict the smoothed monthly mean sunspot numbers in solar cycle 23, and the results are satisfying. The application results also indicate that in comparison to other traditional prediction methods, the prediction method used in this paper has a higher prediction accuracy, thus it has theoretical meaning and practical value for the space environment prediction.

关 键 词：太阳黑子数 时变阈值过程神经网络 时间序列预测 泛函逼近 

分 类 号：P182.41[天文地球—天文学]