CAJ | 학술논문

针对随机时间序列的强不确定性和非线性特征,结合粗糙集理论和成组数据处理的神经网络技术建立了基于粗集的GMDH神经网络预测模型.同时就自然界大多数的随机时间序列数据维数较大的问题,为提高约简效率,提出了基于快速求核和集合近似质量的约简算法,并进行了仿真验证.结果表明,基于粗集的GMDH神经网络预测模型合理可行,约简算法快速有效.
침대수궤시간서렬적강불학정성화비선성특정,결합조조집이론화성조수거처리적신경망락기술건립료기우조집적GMDH신경망락예측모형.동시취자연계대다수적수궤시간서렬수거유수교대적문제,위제고약간효솔,제출료기우쾌속구핵화집합근사질량적약간산법,병진행료방진험증.결과표명,기우조집적GMDH신경망락예측모형합리가행,약간산법쾌속유효.
In view of the random time sequence's strong uncertainty and the misalignment characteristic, GMDH neural network forecast model was established by combining rough set and group data handling. Meanwhile, most of the nature random time sequence has the problem of large data dimension, to improve the reduction efficiency. A reduction algorithm was proposed based on fast seeking nucleus and similar quality of sets, and simulation confirmation was given. The results indicate that the proposed GMDH neural network forecast model is reasonable and feasible, and the reduction algorithm is fast and efficient.