Neural Processing Letters ( IF 2.6 ) Pub Date : 2020-10-22 , DOI: 10.1007/s11063-020-10363-z Lin Wang , Binrong Wu , Qing Zhu , Yu-Rong Zeng
The accurate forecasting of monthly tourism demand can improve tourism policies and planning. However, the complex nonlinear characteristics of monthly tourism demand complicate forecasting. This study proposes a novel approach named ICPSO-BPNN that combines improved chaotic particle swarm optimization (ICPSO) with backpropagation neural network (BPNN) to forecast monthly tourism demand. ICPSO with chaotic initialization and two search strategies, sigmoid-like inertia weight, and linear acceleration coefficients is utilized to search for the appropriate initial connection weights and thresholds necessary to improve the performance of BPNN. Two comparative real-life examples and one extended example are adopted to verify the superiority of the proposed ICPSO-BPNN. Results show ICPSO-BPNN outperforms that of the basic BPNN, autoregressive integrated moving average model, support vector regression, and other popular existing models.
中文翻译:
使用增强型反向传播神经网络预测每月旅游需求
对月度旅游需求的准确预测可以改善旅游政策和规划。但是,月度旅游需求的复杂非线性特征使预测变得复杂。这项研究提出了一种名为ICPSO-BPNN的新方法,该方法结合了改进的混沌粒子群优化(ICPSO)和反向传播神经网络(BPNN)来预测每月的旅游需求。具有混沌初始化和两种搜索策略(类似于S形惯性权重和线性加速度系数)的ICPSO用于搜索适当的初始连接权重和阈值,以改善BPNN的性能。采用两个比较现实的例子和一个扩展例子来验证所提出的ICPSO-BPNN的优越性。结果表明ICPSO-BPNN优于基本BPNN,