Comput. Biol. Med. - An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

Tópicos

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Resumo

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.

Resumo Limpo

paper use particl swarm optim algorithm solv optim paramet estim problem improv nash nonlinear grey bernoulli model term psonngbm propos test forecast perform optim model appli forecast incid hepat b xinjiang china four model tradit gm grey verhulst model gvm origin nonlinear grey bernoulli model ngbm holtwint exponenti smooth method also establish comparison propos model criteria mean absolut percentag error root mean squar percent error predict result show optim nngbm model accur perform better tradit gm gvm ngbm holtwint exponenti smooth method

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