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However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting China\u02bcs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first\u2010order accumulative generating operator (1\u2010AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1\u2010AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.<\/jats:p>","DOI":"10.1155\/2019\/1731262","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T23:30:41Z","timestamp":1572305441000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China\u2019s 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