On comparison of two strategies in net demand forecasting using Wavelet Neural Network

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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Regional logistics demand forecasting: a BP neural network approach

Complex & Intelligent Systems ◽

10.1007/s40747-021-00297-x ◽

2021 ◽

Author(s):

Lijuan Huang ◽

Guojie Xie ◽

Wende Zhao ◽

Yan Gu ◽

Yi Huang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Demand Forecasting ◽

Indicator System ◽

Regional Logistics ◽

Logistics Enterprises ◽

Logistics Demand ◽

Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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Research on Harmonic Detection Method Based on Wavelet Neural Network with a New Structure

2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES) ◽

10.1109/aeees51875.2021.9403003 ◽

2021 ◽

Author(s):

Ming Zhang ◽

Yang Wang ◽

Chen Cheng ◽

Jiaxiong Xu ◽

Shunfan He ◽

...

Keyword(s):

Neural Network ◽

Detection Method ◽

Wavelet Neural Network ◽

Harmonic Detection

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Design of Morlet Wavelet Neural Network for Solving a Class of Singular Pantograph Nonlinear Differential Models

IEEE Access ◽

10.1109/access.2021.3072952 ◽

2021 ◽

pp. 1-1

Author(s):

Kashif Nisar ◽

Zulqurnain Sabir ◽

Muhammad Asif Zahoor Raja ◽

Ag. Asri Ag. Ibrahim ◽

Fevzi Erdogan ◽

...

Keyword(s):

Neural Network ◽

Morlet Wavelet ◽

Wavelet Neural Network

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