Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 49, Issue -, Pages 67-73Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2014.02.008
Keywords
Nonlinear grey Bernoulli model; Particle swarm optimization; Grey model; Hepatitis B
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Funding
- National Natural Science Foundation of China [11201399]
- Academic Discipline Project of Xinjiang Medical University-Health Measurements and Health Economics [XYDXK50780308]
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In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. (C) 2014 Published by Elsevier Ltd.
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