摘要
本文描述了肾衰竭治疗过程中腹膜透析液体重吸收率(PFAR)的预测问题。提出了基于改进遗传神经网络的PFAR预测模型。在文章中分析了PFAR的重要性和透析过程的复杂性,将改进遗传算法用于神经网络初始权重的确定,然后再利用神经网络的局部搜索能力确定最优遗传神经网络模型。这种方法充分利用了遗传算法的全局搜索的能力和神经网络局部快速搜索的优点,使两种方法充分互补。与标准的遗传神经网络仿真比较结果表明,该方法提高了学习速度和预测精度。
This paper addresses the predicting problem of peritoneal fluid absorption rate（PFAR）. An innovative predicting mode/was developed, which employed the improved genetic algorithm embedded in neural network for predicting the important PFAR index in the peritoneal dialysis treatment process of renal failure. The significance of PFAR and the complexity of dialysis process were analyzed. The improved genetic algorithm was used for defining the initial weight and bias of neural network, and then the neural network was used for finding out the optimal predicting model of PFAR. This method utilizes the global search capability of genetic algorithm and the local search advantage of neural network completely. For the purpose of showing the validity of the model, the improved optimal predicting model is compared with the standard hybrid method of genetic algorithm and neural network. The simulation results show that the predicting accuracy of the improved optimal neural network is greatly improved and the learning process needs less time.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2009年第6期1186-1190,共5页
Journal of Biomedical Engineering
基金
广州市科技计划项目资助(2008Z1-D081)
高等学校博士学科点专项科研基金资助课题(200805611070)
关键词
遗传算法
神经网络
预测
腹膜透析
Genetic algorithm Neural network Prediction Peritoneal dialysis
