Interval Emotional Neural Network for Prediction of Kp, AE and Dst geomagnetic activity indices

被引:0
|
作者
Moghadam, R. Ahmadi [1 ]
Yaghoubi, M. [2 ]
机构
[1] Islamic Azad Univ, Mashhad Branch, Dept Comp Engn, Mashhad, Iran
[2] Islamic Azad Univ, Mashhad Branch, Dept Elect & Comp Engn, Mashhad, Iran
关键词
BEL; brain emotional learning; emotional network; geomagnetic storms; emotion; ALGORITHM; SOLAR; MODEL;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, an interval emotional neural network (ENN) is proposed for uncertainty modeling of chaotic time series. The proposed interval ENN consist of 4 main neural blocks including: thalamus (TH), amygdala (AMY), orbitofrontal cortex (OFC) and sensory cortex (SC). In the proposed interval ENN, the main connections located between these components are modeled by interval weights including: TH-AMY, OFC-AMY, SC-OFC and SC-AMY connections. In this paper genetic algorithm (GA) is applied for optimal tuning of interval ENN's weights and a traditional artificial neural network (ANN) is used for comparison purposes. In the Experimental Results Section, chaotic geomagnetic activity Kp, AE and Dst indices are used as a case study. Numerical results indicate the superiority of the proposed interval ENN in term of higher accuracy and lower uncertainty.
引用
收藏
页码:325 / 331
页数:7
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