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Using an Enhanced Feed-Forward BP Network for Predictive Model Building from Students' Data

机译：使用增强型前馈BP网络根据学生数据进行预测模型构建

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Feed-forward, Back Propagation ( BP) Network is a network structure capable of modeling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks, most especially when trained with appropriate algorithms. This study presents an enhancement of this network with a view to boosting its prediction accuracy. The paper proposed a modification of the data partitioning function in the regular feed-forward network. A predictive model is constructed based on the proposed partition, while the second model is based on the partition of the existing network. Both models are trained and simulated with sets of untrained data. The mean absolute error is computed for both models and their error values are compared. Comparison of their results shows that the enhanced network consistently delivers higher accuracy and generalized better than the existing network in its regular structure; as there was a decrease in error from 0.261 to 0.016. The enhanced network has also shown its suitability in the fittings of models from students' data for prediction purposes.
机译：前馈，反向传播（BP）网络是一种网络结构，能够将类别预测建模为输入的非线性组合。该网络已证明适合解决多种复杂的任务，尤其是在使用适当的算法进行培训时。这项研究提出了对该网络的增强，以期提高其预测准确性。本文提出了对常规前馈网络中数据分区功能的一种修改。基于建议的分区构造了一个预测模型，而第二个模型则基于现有网络的划分。两种模型都使用未训练的数据集进行了训练和仿真。计算两个模型的平均绝对误差，并比较其误差值。他们的结果比较表明，增强型网络在常规结构上始终具有比现有网络更高的准确性和更好的泛化能力。因为误差从0.261降低到0.016。增强型网络还证明了其在根据学生数据进行模型拟合中的适用性，以进行预测。

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