Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Authors

  • Yijian Zhang

DOI:

https://doi.org/10.62051/r88re536

Keywords:

Consumer Confidence Index; BP Neural Network; DEGWO Algorithm; Error Analysis.

Abstract

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.

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References

HUANG Qi, Chen Hanying, Liu Yan,et al. A Mobile Robot Path Planning Based on Multi-strategy Fusion Gray Wolf Algorithm[J]. Journal of Air Force Engineering University,3024,25(3):112-120.

Dehghani M, Trojovsk Y P. Osprey Optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems[J]. FRONTIERS in mechanical engineering,2023,8:1126450.

HE Z H, JIN G, Wang y j. A novel Grey Wolf Optimizer and Its Applications 5G Freguency Selection Surface Design[D].Frontiers of Information Technology,2022,23(9):1338-1253.

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Published

08-07-2024

How to Cite

Zhang, Y. (2024). Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm. Transactions on Social Science, Education and Humanities Research, 9, 147-153. https://doi.org/10.62051/r88re536