Simultaneously, this model performance was evaluated from the perspective of consistence and computational complexity.

同时，从一致性和计算复杂度方面对模型性能进行了评价。

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Experiments have been conducted using SVM-KM toolbox with 50 group data for training model and 15 group data for verifying the model performance.

应用SVM-KM对该模型进行实验研究，利用50组数据对模型进行训练并验证其学习性能，利用另外15组数据验证其泛化能力。

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Through computer simulation, samples, BP algorithms and the influence of network structure neurula on model performance have been discussed as well as the improving measures.

通过计算机实验，讨论样本、学习算法和网络结构等对神经网络预测模型性能的影响及其改进措施。

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