NDLI: Optimal predicting method of peritoneal fluid absorption rate using genetic algorithm embedded in neural network

Content Provider	IEEE Xplore Digital Library
Author	Mei Zhang Yue-Ming Hu Tao Wang
Copyright Year	2005
Description	Author affiliation: Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Mei Zhang; Yue-Ming Hu)
Abstract	This paper addresses the predicting problem of peritoneal fluid absorption rate in the peritoneal dialysis treatment process of renal failure. An innovative predicting model was developed in this paper, which employed genetic algorithm embedded in neural network to predict the important PFAR index in the peritoneal dialysis treatment process of renal failure. The significance of PFAR and the complexity of peritoneal process are analyzed. Genetic algorithm is used to initial weight and bias of neural network, and then optimal predicting model of PFAR was built based on neural network. This method utilizes the global search capability of genetic algorithm and local search advantage of neural network completely. To show the validity of the model, the optimal predicting model is compared with conventional artificial neural network and multivariate regression method. The simulation results show that the predicting accuracy of the optimal neural network is greatly improved and learning process needs less time.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4167
Ending Page	4172
File Size	279314
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Absorption Genetic algorithms Intelligent networks Neural networks Artificial neural networks Predictive models Medical treatment High definition video Equations Educational institutions peritoneal dialysis Genetic algorithm neural network prediction
Content Type	Text
Resource Type	Article

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