GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments by Peng Gao, Jinzhen Fang, Junlin He, Shuang Ma, Guanghua Wen, Zhen Li

“…Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. …”
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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

“…Compared with the second-best performing informer model， the proposed model achieved reductions of 24.0%， 23.1%， and 28.5% in the mean absolute error， mean absolute percentage error， and root mean square error， respectively. …”
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Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series by CHEN Bowen, DENG Jian, ZHU Qianliu

“…Experimental results showed that Periodformer achieved reductions in both Mean Absolute Error (MAE) and Mean Squared Error (MSE) of 5.56% and 11.85%, respectively, compared to the existing Transformer model, while exhibiting strong robustness against data noise.…”
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GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT Integration by Ting Liu, Yuan Liu

“…Additionally, two self-supervised tasks are designed for improved model accuracy and robustness. (3) Results: The fine-tuned model had comparatively optimal performance metrics with significant reductions in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). (4) Conclusions: Ultimately, the integration of this model into travel time prediction, based on two large-scale real-world trajectory datasets, demonstrates enhanced performance and computational efficiency.…”
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Adaptive Nonlinear Proportional–Integral–Derivative Control of a Continuous Stirred Tank Reactor Process Using a Radial Basis Function Neural Network by Joo-Yeon Lee, Gang-Gyoo Jin, Gun-Baek So

“…For disturbance changes, the proposed method reduced the peak magnitude (<i>M_peak</i>) by 4.9%, recovery time (<i>t_rcy</i>) by 23.6%, and integral absolute error by 16.2%. Similarly, for parameter changes, the reductions were 3.0% (<i>M_peak</i>), 26.4% (<i>t_rcy</i>), and 24.4% (<i>IAE</i>).…”
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Seasonally Adaptive VMD-SSA-LSTM: A Hybrid Deep Learning Framework for High-Accuracy District Heating Load Forecasting by Yu Zhang, Keyong Hu, Lei Lu, Qingqing Yang, Min Fang

“…Notably, the MAPE values are reduced to 1.3824%, 0.9549%, 6.4018%, and 1.3272%, with average error reductions of 9.4873%, 3.8451%, 6.6545%, and 6.5712% compared to alternative models. …”
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IMA-FLADRC-Based Electric-Driven High-Speed Maize Precision Seeding Control Strategy and System by Song Wang, Shujuan Yi, Bin Zhao, Yifei Li, Dongming Zhang, Tao Chen, Wensheng Sun

“…Simulation results show that the IMA-FLADRC-based guiding motor achieves no overshoot or static error, with an adjustment time of 1.121s, a maximum disturbance error of 12.016 r/min, and a disturbance recovery time of 0.008s, while the seeding motor exhibits no static error, minimal overshoot of 0.059%, an adjustment time of 0.374s, a maximum disturbance error of 2.563 r/min, and a disturbance recovery time of 0.003s, outperforming four other strategies. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…The experimental results indicate that compared with the traditional model, the proposed model achieves average reductions of 67.30%, 47.68%, 48.42%, and 48.79% in the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively, demonstrating higher prediction accuracy and robustness. …”
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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.…”
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Structural Equation Modeling Approaches to Estimating Score Dependability Within Generalizability Theory-Based Univariate, Multivariate, and Bifactor Designs by Walter P. Vispoel, Hyeryung Lee, Tingting Chen

“…Although general-factor effects were dominant, subscale viability was supported in all cases, with transient measurement error leading to the greatest reductions in score accuracy. …”
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A deep learning framework for prediction of crop yield in Australia under the impact of climate change by Haydar Demirhan

“…The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. …”
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GRU-LSTM model based on the SSA for short-term traffic flow prediction by Changxi Ma, Xiaoyu Huang, Yongpeng Zhao, Tao Wang, Bo Du

“…Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in R2 of 0.5%–17.5%. …”
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A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang, Patrick Wong

“…The experimental results demonstrate that EOLSO significantly outperforms the SO, achieving reductions of 43.83% in the Sum of Squares Error (SSE), 30.73% in the Mean Absolute Error (MAE), and 25.05% in the Root Mean Square Error (RMSE). …”
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Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm by Bin Cui, Yuanming Chen, Xiaobin Hong, Hao Luo, Guanqiao Chen

“…The results indicate that for straight path tracking, the DDPG-MPC algorithm achieves 37% and 21% reductions in the average cross error and heading angle error, respectively, compared to the ALOS-PID algorithm. …”
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Elastodynamic modeling and analysis of a 4SRRR overconstrained parallel robot by B. Wang, Y. Zhao, C. Yang, X. Hu, Y. Zhao

“…Further increasing the number of rod divisions results in diminishing reductions in the error.</p>…”
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Design and Analysis of a Sowing Depth Detection and Control Device for a Wheat Row Planter Based on Fuzzy PID and Multi-Sensor Fusion by Yueyue Li, Bing Qi, Encai Bao, Zhong Tang, Yi Lian, Meiyan Sun

“…Experimental results demonstrate that under representative test conditions, the system achieves excellent sowing depth control performance; average error reductions of 10.7%, 22.9%, and 9.6% were observed when using fuzzy PID control versus no control. …”
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Improving seasonal precipitation forecasts in the Western United States through statistical downscaling by B Vernon, W Zhang, Y Chikamoto

“…The greatest error reductions in the downscaled product, measured by root mean squared error (RMSE), were observed at low to mid-elevations (500–2000 meters), with 50%–70% improvement relative to the original forecasts. …”
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Improving Trajectory Tracking of Differential Wheeled Mobile Robots With Enhanced GWO-Optimized Back-Stepping and FOPID Controllers by Li Qiang, Hooi Hung Tang, Nur Syazreen Ahmad

“…This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize steady-state error and enhance responsiveness to larger deviations. …”
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Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study by Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao, Xiaodong Shen

“…Results show that the proposed method significantly outperforms the LSTM model without decomposition and other benchmark models in prediction accuracy, with the Root Mean Square Error (RMSE) reductions ranging from 40.26% to 74.18% and the Modified Mean Absolute Percentage Error (MMAPE) reductions from 37.75% to 73.41%. …”
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Correction of the Contribution of Scattered Photon Radiation to the Ionization Chamber Readings During X-Ray Radiation Quality Assessment by A. A. Zaharadniuk, K. G. Senkovsky, R. V. Lukashevich

“…Reduction of the systematic error when determining the characteristics of the reference X-ray radiation fields is an essential task according to the ISO 4037-1:2019 standard. …”
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