【摘要】 为提高空闲预测的准确性,提出基于灰色模型的空闲预测算法.该算法对预测样本的数量和分布特征没有苛刻要求,仅使用少量样本数据就能够进行预测.预测过程可以动态调整预测参数,实现自适应预测,提高预测的准确性.实验结果表明,使用该算法进行预测得到的预测值接近真实值,准确性高于指数平均预测算法.更多还原

【Abstract】 An algorithm based on grey model to predict idle period for DPM is proposed in order to improve the accuracy of predicted results.The algorithm needs only a few samples for prediction and does not have strict requirements on the quantity and distribution characteristics of samples.A self-adaptive prediction is implemented to promote the accuracy of predicted results by adjusting parameters dynamically.Experimental results show that the predicted results by use of the algorithm approximate well to real ones and are more accurate compared to algorithms based on the exponential average method.更多还原