School of Public Health, Xinjiang Medical University, Urumqi 830011, People׳s Republic of China; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, People׳s Republic of China.
School of Public Health, Xinjiang Medical University, Urumqi 830011, People׳s Republic of China; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, People׳s Republic of China.
Comput Biol Med. 2014 Jun;49:67-73. doi: 10.1016/j.compbiomed.2014.02.008. Epub 2014 Feb 20.
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.
本文通过使用粒子群优化算法解决最优参数估计问题，提出了一种改进的纳什非线性灰色贝努利模型，称为 PSO-NNGBM(1,1)。为了测试预测性能，将优化模型应用于预测中国新疆的乙肝发病率。在平均绝对百分比误差和均方根百分比误差的标准下，还建立了四个模型，传统的 GM(1,1)、灰色 Verhulst 模型（GVM）、原始非线性灰色贝努利模型（NGBM(1,1)）和霍尔特-温特斯指数平滑法，与所提出的模型进行比较。预测结果表明，优化后的 NNGBM(1,1)模型比传统的 GM(1,1)、GVM、NGBM(1,1)和霍尔特-温特斯指数平滑法更准确，性能更好。
