WASP Neuronet Activated by Bipolar-Sigmoid Functions and Applied to Glomerular-Filtration-Rate Estimation
By combining two fast training methods, i.e., the weights-direct-determination (WDD) method and Levenberg-Marquardt method, this paper proposes a novel training algorithm called weights and structure policy (WASP) for the three-layer feedforward neuronet, in addition to the algorithm of weights and structure determination (WASD). Note that the pruning-while-growing and second-pruning techniques are developed and exploited in the WASP algorithm with the aim of achieving a neuronet with a simple and economical structure. In order to verify the WASP efficacy and to address the problem of chronic kidney disease (CKD) for clinical applications in China, numerical experiments about estimating glomerular filtration rate (GFR) by the WASP neuronet and traditional GFR-estimation equations are conducted and compared. The experiment results show that the WASP training speed is fast and that the estimating accuracy via the WASP neuronet is around 20% higher than those via traditional GFR-estimation equations. The WASP efficacy is thus demonstrated with a significant value in GFR estimation of CKD for clinical applications.
Weights Direct Determination Levenberg-Marquardt Algorithm Weights and Structure Policy Chronic Kidney Disease Glomerular Filtration Rate
Yunong Zhang Sitong Ding Xun Liu Jinrong Liu Mingzhi Mao
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-se
国际会议
长沙
英文
172-177
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）