Zhang Mei, Hu Yueming, Wang Tao, Zhu Jinhui
Engineering Research Center of Precision Electronic Manufacturing Equipments, Ministry of Education, College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2009 Dec;26(6):1186-90.
This paper addresses the predicting problem of peritoneal fluid absorption rate(PFAR). An innovative predicting model 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.
本文探讨了腹膜液吸收率（PFAR）的预测问题。开发了一种创新的预测模型，该模型采用嵌入神经网络的改进遗传算法来预测肾衰竭腹膜透析治疗过程中的重要PFAR指标。分析了PFAR的意义和透析过程的复杂性。使用改进的遗传算法来定义神经网络的初始权重和偏差，然后利用神经网络找出PFAR的最优预测模型。该方法充分利用了遗传算法的全局搜索能力和神经网络的局部搜索优势。为了验证模型的有效性，将改进的最优预测模型与遗传算法和神经网络的标准混合方法进行了比较。仿真结果表明，改进的最优神经网络的预测精度有了很大提高，学习过程所需时间更少。
