基于神经网络的方法预测生物活性介质网络调控矽肺纤维化的研究

目的:探讨矽肺纤维化同生物活性介质之间的关系。方法:利用Delphi语言编制了BP人工神经网络模型计算机程序,建立并分析了矽肺胶原纤维预测的数学模型。结果:选定网络隐含层节点为9,初始权值阈值约为(-0.2,0.2),最大相对误差为4%,最小相对误差为0.2%。结论:应用神经网络具有较好的预测效果,可为临床医学研究提供一个很好的研究思路。

Prediction of Pulmonary Fibrosis Regulated by Bioactivity Mediums Based on Neutral Network

Objective: To study the relation of pulmonary fibrosis and bioactivity mediums.Method: To apply BP neutral network based on Delphi computer language to construct and analyze math model of pulmonary fibrosis prediction.Result: Max relative error and min relative error are respective 4 percent and 0.2 percent when the number of network hidden nodes is 9 and threshold of initial weight is between-0.2 and 0.2.Conclusion: The result shows that there is a better prediction result and the method can provide a better research direction for the clinical medicine.