A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast

IF 2.4 Q2 ENGINEERING, MECHANICAL

Nonlinear Engineering - Modeling and Application Pub Date : 2022-01-01 DOI:10.1515/nleng-2022-0025

Zheng Sun, Xina Li, Hongtao Zhang, M. Ikbal, A. R. Farooqi

引用次数: 0

Abstract

Abstract Neural network modeling for nonlinear time series predicts modeling speed and computational complexity. An improved method for dynamic modeling and prediction of neural networks is proposed. Simulations of the nonlinear time series are performed, and the idea and theory of optimizing the initial weights and threshold of the GA algorithm are discussed in detail. It has been proved that the use of GA-BP neural network in cigarette sales forecast is 80% higher than before, and this method has higher accuracy and accuracy than the gray system method.

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非线性时间序列预测的GA-BP神经网络及其在卷烟销售预测中的应用

非线性时间序列的神经网络建模预测了建模速度和计算复杂度。提出了一种改进的神经网络动态建模与预测方法。对非线性时间序列进行了仿真，详细讨论了遗传算法初始权值和阈值优化的思想和理论。实践证明，GA-BP神经网络在卷烟销售预测中的应用比以前提高了80%，并且该方法比灰色系统方法具有更高的准确度和准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nonlinear Engineering - Modeling and Application Multiple-

CiteScore

6.20

自引率

3.60%

发文量

审稿时长

44 weeks

期刊介绍： The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.