time complexity model

基本解釋

Experimental results show that the URSIM is feasible. Compared with the existed methods, the model is efficient and can reduce time complexity.
實例分析證明該模型是可行的，與已知方法相比具有較高的執(zhí)行效率和較低的時間復雜度。
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，演化過程中逐步增加商務模式相互作用規(guī)模復雜性的同時，應盡可能均勻分布商務模式組分間的相互作用關系；
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.
試驗結果表明，加入聚類分析的徑向基神經(jīng)網(wǎng)絡模型提高了連續(xù)預測的趨勢準確率，降低了時間代價，并減小了模型的復雜度。