title: [An optimal predicting method based on improved genetic algorithm embedded in neural network and its application to peritoneal dialysis].
abstract: 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.
authors.affiliation: 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. auzhangm@163.com
authors.forename: Mei
authors.initials: M
authors.lastname: Zhang
authors.forename: Yueming
authors.initials: Y
authors.lastname: Hu
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.forename: Jinhui
authors.initials: J
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005544:Forecasting; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010530:Peritoneal Dialysis / Q000379:methods*