grey time sequential combined model gmar组合模型
Analysis and modeling are made for the boring error by using time sequential analysis way, and corresponding ar error model is established.
用时间序列分析方法对镗削加工误差进行了分析和建模，建立了相应的AR误差模型。
The result of this experiment shows that the modified RBF neuro-network increases trend accuracy in sequential predicting, while debasing the cost of time and reducing the complexity of the model.
试验结果表明，加入聚类分析的径向基神经网络模型提高了连续预测的趋势准确率，降低了时间代价，并减小了模型的复杂度。
The computing model result in the separation of the influence of machine utilization, sequential job service rate, and parallel task allocation to the parallel task completion time.
通过这个计算模型可从机器利用率，连续工作服务速率，并行任务分配等方面对并行任务完成时间进行预测。
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