A Pork Price Prediction Model Based on a Combined Sparrow Search Algorithm and Classification and Regression Trees Model DOI Creative Commons
Jing Qin, Degang Yang, Wenlong Zhang

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(23), P. 12697 - 12697

Published: Nov. 27, 2023

The frequent fluctuation of pork prices has seriously affected the sustainable development industry. accurate prediction can not only help practitioners make scientific decisions but also them to avoid market risks, which is way promote healthy Therefore, improve accuracy prices, this paper first combines Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification Regression Trees (CART), establish an SSA-CART optimization model for predicting prices. Secondly, based on Sichuan price data during 12th Five-Year Plan period, linear correlation between piglet, corn, fattening pig feed, was measured using Pearson coefficient. Thirdly, MAE fitness value calculated by combining validation set training set, hyperparameter “MinLeafSize” optimized via SSA. Finally, a comparative analysis performance White Shark Optimizer (WSO)-CART CART Simulated Annealing (SA)-CART demonstrated that best (compared with single decision tree, R2 increased 9.236%), conducive providing support prediction. great practical significance stabilizing production, ensuring growth farmers’ income, promoting sound economic development.

Language: Английский

State of charge estimation of lithium ion battery for electric vehicle using cutting edge machine learning algorithms: A review DOI
Sairaj Arandhakar, Jayaram Nakka

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 103, P. 114281 - 114281

Published: Oct. 23, 2024

Language: Английский

Citations

3
Performance prediction and optimization of annular thermoelectric generators based on a comprehensive surrogate model DOI
Aoqi Xu, Changjun Xie, Liping Xie

et al.

Energy, Journal Year: 2023, Volume and Issue: 290, P. 130195 - 130195

Published: Dec. 29, 2023

Language: Английский

Citations

8
Artificial neural network based decentralized current-sharing control for parallel connected DC-DC converters in DC microgrid application DOI

Musharraf Ali Saddriwala,

Mohd Alam

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109731 - 109731

Published: Oct. 14, 2024

Language: Английский

Citations

2
High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm DOI Creative Commons
Zhihang Zhang, Siliang Chen, Languang Lu

et al.

Batteries, Journal Year: 2023, Volume and Issue: 9(6), P. 333 - 333

Published: June 20, 2023

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into pack systems. It enhances volumetric power density, significantly reduces costs, and widely utilized in electric vehicles. However, the flat open circuit voltage significant polarization differences under wide operational temperatures are challenging for accurate modeling of management systems (BMSs). In particular, inaccurate state charge (SOC) estimation may cause overcharging over-discharging risks. To accurately perceive SOC LiFePO4 batteries, method based on backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm proposed. BPNN network model utilizes to update parameters, while EKF an optimal algorithm. Firstly, dynamic working condition tests, including New European Driving Cycle (NEDC) high-speed (HSW) conducted temperature range (−25–43 °C). HSW conditions refer simulated operating mimics driving vehicle highway. minimum system used as output training model. We derive gain by combining voltage. Additionally, employed correct value using error information. Concerning long calculation intervals, capacity errors, initial current sampling maximum RMSE 3.98% at −20 °C NEDC, 3.62% 10 1.68% 35 HSW. proposed applied different operations, demonstrating high robustness. This BPNN-EKF has potential embedded BMS practical applications.

Language: Английский

Citations

5
A Pork Price Prediction Model Based on a Combined Sparrow Search Algorithm and Classification and Regression Trees Model DOI Creative Commons
Jing Qin, Degang Yang, Wenlong Zhang

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(23), P. 12697 - 12697

Published: Nov. 27, 2023

The frequent fluctuation of pork prices has seriously affected the sustainable development industry. accurate prediction can not only help practitioners make scientific decisions but also them to avoid market risks, which is way promote healthy Therefore, improve accuracy prices, this paper first combines Sparrow Search Algorithm (SSA) and traditional machine learning model, Classification Regression Trees (CART), establish an SSA-CART optimization model for predicting prices. Secondly, based on Sichuan price data during 12th Five-Year Plan period, linear correlation between piglet, corn, fattening pig feed, was measured using Pearson coefficient. Thirdly, MAE fitness value calculated by combining validation set training set, hyperparameter “MinLeafSize” optimized via SSA. Finally, a comparative analysis performance White Shark Optimizer (WSO)-CART CART Simulated Annealing (SA)-CART demonstrated that best (compared with single decision tree, R2 increased 9.236%), conducive providing support prediction. great practical significance stabilizing production, ensuring growth farmers’ income, promoting sound economic development.

Language: Английский

Citations

4