Lei Wang

Prof. Dr. Lei Wang

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2019 in Chaos, Solitons & Fractals
Verified
Journal Article
Closed access
LWLei Wang
Lei Wang
SLShouwei Li
Shouwei Li
TCTingqiang Chen
Tingqiang Chen

1 July 2019 in Physica A: Statistical Mechanics and its Applications
Verified
Journal Article
Closed access
LWLei Wang
Lei Wang
SLShouwei Li
Shouwei Li
CWChaoqun Wu
Chaoqun Wu

5 July 2025 in Finance Research Letters
Verified
Journal Article
Closed access
LWLei Wang
Lei Wang
YWYan Wang
Yan Wang
JWJining Wang
Jining Wang
Lean YuLean Yu
Lean Yu

22 February 2021 in The European Physical Journal B
Verified
Journal Article
Closed access
PDF file
ZPZicheng Pan
Zicheng Pan
QMQianting Ma
Qianting Ma
JDJunfei Ding
Junfei Ding
LWLei Wang
Lei Wang

25 December 2024 in Sustainability
Verified
Journal Article
Open access
PDF file
JWJining Wang
Jining Wang
XZXuewei Zhao
Xuewei Zhao
LWLei Wang
Lei Wang
Traditional BP neural networks frequently encounter local optima during carbon price forecasts. This study adopts a hybrid approach, combining a genetic algorithm and particle swarm optimization (GA-PSO) to improve the BP neural network, resulting in the creation of a GA-PSO-BP neural network model. Seven critical factors were identified affecting carbon prices, and we utilized data on carbon emission trading prices from China for the analysis. Compared to traditional BP neural network models, including GA-BP neural network models optimized solely with genetic algorithms and PSO-BP neural network models enhanced through particle swarm optimization, the findings reveal that the GA-PSO-BP neural network model demonstrates superior performance in terms of precision and robustness. Furthermore, it demonstrates advancements across various error evaluation metrics, thus delivering more accurate forecasts. Offering precise carbon price predictions, the enhanced GA-PSO-BP neural network model proves to be a valuable tool for analyzing the market and making decisions in the carbon pricing sector.

1 November 2019 in Physica A: Statistical Mechanics and its Applications
Verified
Journal Article
Closed access
SLShouwei Li
Shouwei Li
MLMin Liu
Min Liu
LWLei Wang
Lei Wang
KYKun Yang
Kun Yang