time complexity model

基本解释

Experimental results show that the URSIM is feasible. Compared with the existed methods, the model is efficient and can reduce time complexity.
实例分析证明该模型是可行的，与已知方法相比具有较高的执行效率和较低的时间复杂度。
Therefore enterprise should arrange the interaction among components of business model at the same time of increasing complexity of the business model scale;
因此，演化过程中逐步增加商务模式相互作用规模复杂性的同时，应尽可能均匀分布商务模式组分间的相互作用关系；
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。