改进BP神经网络对高血压病因的研究

Xiangmin Dong, Wang Ping
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引用次数: 2

摘要

基于L-M (Levenberg-Marquard)算法神经网络的改进神经网络应用于高血压因素分析模型。它可以弥补传统BP算法神经网络收敛速度慢的缺点。这个模型可以确定哪些因素是导致高血压的主要原因。采用点阵模糊贴近度评价法和专家评分法对影响高血压的各种因素数据进行量化。我们使用matlab进行编程和仿真。结果表明:改进后的BP神经网络能正确判断导致高血压的主要因素,预测值与实际值误差很小。它达到了预期的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the causes of hypertension with improved BP neural network
An improved neural network based on L-M (Levenberg-Marquard) algorithm neural network has been applied to the model for the analysis of factors on Hypertension. It can remedy the shortcoming of the slow convergence rate of traditional BP algorithm neural network. This model can determine which factors are the main reasons for high blood pressure. We have adopted the lattice fuzzy close-degree assessment and expert scoring method which quantified the various data of the factors on high blood pressure. We used the matlab to program and simulate. The results showed that: the improved BP neural network, can determine the main factors which causing the high blood pressure correctly, the error between the Predictive value and the actual value is very small. it reached the desired goal.
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