人工神经网络在选举参与度估计中的应用
Application of Artificial Neural Networks in Estimating Participation in Elections
摘要：
证明了人工神经网络在预测和分析传统方法和静力学无法解决的流动问题上是相当有效的。本文利用输入输出层均具有tan-sigmoid传递函数的两层前馈网络，预测了伊朗伊斯兰共和国未来总统选举中Kohgiloye省和Boyerahmad省的公众参与率，预测准确率达到91%。混淆矩阵和ROC图等参与度评价标准已被我们的主张所认可。
英文摘要：
It is approved that artificial neural networks can be considerable effective in anticipating and analyzing flows in which traditional methods and statics are not able to solve. in this article, by using two-layer feedforward network with tan-sigmoid transmission function in input and output layers, we can anticipate participation rate of public in kohgiloye and boyerahmad province in future presidential election of islamic republic of iran with 91% accuracy. the assessment standards of participation such as confusion matrix and roc diagrams have been approved our claims.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
