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A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

INFORMATION SCIENCES 2025年 686卷

作者： Karahasan, Ozlem Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

关键词： Simple recurrent artificial neural network Exponential smoothing Sine cosine optimization algorithm particle swarm optimization algorithm Stock exchange Forecasting

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Application of particle swarm optimisation algorithm incorporating frog-leaping algorithm in optimal scheduling for production management in manufacturing plant

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 1721-1735页

作者： Wu, Mingming Wuhu Univ Sch Automot Engn & Intelligent Mfg Wuhu 241000 Peoples R China

The goal of management optimization scheduling in manufacturing plants is to improve machining efficiency and reduce costs, which is one of the research hotspots in the current era. The study proposed a particle swarm optimization algorithm incorporating the frog-leaping algorithm, which combined the grouping mechanism with the global search to improve the search speed of the algorithm, and also incorporated the mutation and crossover ideas of the genetic algorithm. To enhance the machining efficiency while minimizing time and resource requirements, two buffering mechanisms were used for the machining process in this algorithm. The algorithm ultimately achieved the optimal solution of 1170 around the 10th generation, according to experiments, which reduced the maximum machining time by 22%, the average production cycle time by 23%, the machine utilization rate reached 69%, and the percentage of the optimal relative error was almost less than 4%. Additionally, the algorithm's average relative error fluctuation is less than that of the other algorithms, indicating that this algorithm is more stable. This result shows that the particle swarm optimization algorithm incorporating the frog hopping algorithm in this study has good practical value in optimizing the scheduling of production management in manufacturing workshops for improving the processing efficiency and reducing the cost, which is beneficial to the development of the manufacturing industry.

关键词： Job shop scheduling particle swarm optimization algorithm Frog-leaping algorithm Buffering technique Industrial revolution

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Machine learning-based improved plastic stress distribution method for strength prediction of CFST members

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STRUCTURES 2025年 74卷

作者： Zhou, Zheng Yang, Lifu Zhou, Xuhong Shi, Caijun Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China

The plastic stress distribution (PSD) method is used as the primary method in typical design codes for calculating the strength of concrete-filled steel tubular (CFST) members. However, the PSD method can only be applied to members with limited ranges of material strengths and cross-sectional slenderness ratios, and the calculation methods are lacking or relatively complicated for members beyond the limitations. In this work, an improved PSD method is proposed for the strength prediction of CFST members with different cross-sectional slenderness ratios and material strengths, by using the concrete strength factor fi1 to consider the possible compressive strength increase or reduction of the concrete due to the confinement or local buckling from the steel tube and the steel strength factor fi2 to consider the potential compressive strength reduction of the steel tube due to local buckling. The direct inverse analysis using the improved particle swarm optimization algorithm is then conducted to identify the strength factors fi1 and fi2 based on the extensive experimental results. Machine learning algorithms are finally explored to train the rules between the strength factors (fi1 and fi2) and the member properties including the diameter or width and depth, wall thickness, and material strength, and the predicted results are in good agreement with the experimental results. These results further specify the accurate plastic stress distribution, and provide a strong justification for possible changes to the design codes to expand the use of the PSD method to all types of CFST members.

关键词： Concrete-filled steel tubes Plastic stress distribution method Direct inverse analysis particle swarm optimization algorithm Machine learning algorithm

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Active Disturbance Rejection Based Adaptive Dynamic Surface Control for Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8607-8617页

作者： Zhou, Lan Sun, Yongbo He, Yong Li, Hongyi Zhang, Xian-Ming Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411201 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

This paper presents an adaptive dynamic surface control (DSC) method for nonlinear systems with multiple disturbances, parameter uncertainties, and unknown nonlinear dynamics, using a reduced-order extended state observer (ROESO). Compared to related methods, this proposed approach offers several distinct advantages: (i) The method does not require knowledge of the upper bound function for unknown nonlinear dynamics, allowing the controlled plant to not be bounded-input bounded-state, thus expanding the applicability of the DSC method;(ii) By utilizing known model information, an ROESO is designed to handle mismatched uncertainties and disturbances, reducing the load on the observer and enhancing its capability to suppress unknown nonlinear dynamics;(iii) This method employs an adaptive output-feedback DSC approach, which is more practical and easier to implement than state-feedback methods;and (iv) The observer gain, adaptive gain, and DSC gain are simultaneously optimized using a particle swarm optimization algorithm. Additionally, detailed stability analysis is provided, and simulations and comparative experiments are conducted on a rotational system to demonstrate the efficacy and superiority of the proposed method.

关键词： Uncertainty Nonlinear dynamical systems Backstepping Observers Heuristic algorithms Adaptive control Complexity theory Attenuation Adaptation models Uncertain systems Mismatched disturbances dynamic surface control reduced-order extended state observer particle swarm optimization algorithm

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Optimizing Image Watermarking with Dual-Tree Complex Wavelet Transform and particle swarm Intelligence for Secure and High-Quality Protection

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1315-1315页

作者： Bsoul, Abed Al Raoof Ismail, Alaa Bani Yarmouk Univ Informat Syst Dept Irbid 21163 Jordan Yarmouk Univ Comp Sci Dept Irbid 21163 Jordan

Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively.

关键词： watermarking dual-tree complex wavelet transform particle swarm optimization algorithm imperceptibility

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Application of Hybrid Engine Modeling Method Based on Neural Network Group and PSO with Adaptive Inertia Factor in Engine Calibration

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2025年第3期26卷 829-846页

作者： Shi, Xiuyong Wei, Jiande Wang, Haoyu Liu, Hua Jiang, Degang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Nanchang Automot Inst Intelligence & New Energy Nanchang 330052 Peoples R China

The traditional hybrid engine calibration method has low efficiency and high cost of manpower and material resources, which can not meet the calibration requirements of complex engine electronic control system. The calibration method based on a mathematical model can greatly reduce the test workload and improve efficiency. Therefore, the black-box model of the engine is constructed by using the results of Spearman correlation analysis, and nine variables are selected as input, at the same time five variables are used as outputs. The improved RSR-BPNNG neural network group method is used to construct the hybrid engine economy and emission model. The model prediction results show that the R2 value of fuel consumption prediction reaches 0.9975, and the R2 value of NOx emission prediction reaches 0.9933, which achieves high precision modeling. On this basis, the performance of the engine under the WHSC cycle is simulated and optimized by the improved adaptive PSO algorithm. The optimization results show that the NOx emission of the engine is reduced by 8.16%, and the fuel consumption is reduced by 4.55%.

关键词： Hybrid vehicles Engine calibration Neural network particle swarm optimization algorithm

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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第三十一届中国控制会议

作者： ZHOU Wei~1,BU Yan-ping~2 1.School of Business,East China University of Science and Technology,Shanghai,200237,P.R.China 2.School of Technology,Shanghai Jiaotong University,Shanghai,201101,P.R.China

A novel cultural algorithm based on particle swarm optimization(PSO) algorithm was proposed in this *** analyzing the partner selection problems of virtual enterprise,the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and *** are certain number partners of each sub-task in virtual enterprise environment. The objective is,by selecting the optimal combination of partners,to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO *** results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3625-3625页

作者： Liu, Wei Cao, Yu Liaoning Petrochem Univ Sch Informat Sci & Control Engn Fushun 113005 Peoples R China

In busy offshore waters with high vessel density and intersecting shipping lanes, the risk of collisions and accidents is significantly increased. To address the problem of insufficient feature extraction capability of traditional recurrent neural networks (RNNs) in ship trajectory prediction in busy nearshore areas, this paper proposes a hybrid model based on particle swarm optimization (PSO), Convolutional Neural Networks (CNN), Residual Networks, Attention Mechanism, and Gated Recurrent Units (GRU), named PSO-CNN-RGRU-Attention, for ship trajectory prediction. This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. The effectiveness and practicality of the model have been fully verified. Experimental results show that, compared to the PSO-CNN-GRU model, the proposed model improves the longitude by 7.8%, 3.4%, and 1.7% in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), respectively, and improves the latitude by 48.3%, 62.9%, and 39.2%, respectively. This has significantly contributed to enhancing the safety of ship navigation in the Bohai Strait.

关键词： AIS information particle swarm optimization algorithm convolutional neural network attention mechanism trajectory prediction

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Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Mode Decomposition and ED-LSTM

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BATTERIES-BASEL 2025年第3期11卷 86-86页

作者： Song, Bingzeng Yue, Guangzhao Guo, Dong Wu, Hanming Sun, Yonghai Li, Yuhua Zhou, Bin Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Natl Engn Lab Mobile Source Emiss Control Technol Tianjin 300300 Peoples R China Jinan Heating Grp CO LTD Jinan 250011 Peoples R China Shandong Agr Univ Coll Mech & Elect Engn Tai An 271017 Peoples R China State Key Lab Intelligent Transportat Syst Beijing 100088 Peoples R China

The prediction of remaining useful life (RUL) of lithium-ion batteries is key to the reliability assessment of batteries and affects safe application of batteries. This article introduces a CEEMDAN-RF-MHA-ED-LSTM method. Using CEEMDAN, the battery capacity data were decomposed to obtain intrinsic mode functions (IMFs), and the weight of each IMF was obtained via the random forest (RF) algorithm. The LSTM neural network was used, the encoder-decoder (ED) structure was introduced, the multi-head attention (MHA) mechanism was used to construct a network model, and the particle swarm optimization (PSO) algorithm was used to optimize the model parameters. Each IMF was input into the model, and the obtained forecast results were weighted and reconstructed to obtain the final forecast data. This method was validated on the battery dataset released by NASA. Compared with that of the single LSTM model, the mean absolute error of the proposed method decreases by 74%, 62%, 71%, and 55% on the No. 05, 06, 07, and 18th battery datasets, respectively. The root mean square error decreased by 72%, 59%, 70%, and 54%, and the mean absolute percent error decreased by 75%, 65%, 71%, and 58%, respectively. This method can accurately predict battery RUL.

关键词： lithium-ion battery life prediction modal decomposition particle swarm optimization algorithm long short-term memory network

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Application of Genetic and particle swarm optimization algorithms to Portfolio optimization Problem: Borsa Istanbul and Crypto Money Exchange 4

Application of Genetic and Particle Swarm Optimization Algor...

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Huseyinov, Ilham Ulucay, Samed Istanbul Aydin Univ Yazilim Muhendisligi Bolumu Istanbul Turkey Istanbul Aydin Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728139647

particle optimization (PSO) algorithm and genetic (GA) algorithms are widely used in a variety of optimization problems. Portfolio optimization problems offer minimum risk and maximum profit-based solutions to help investors on optimal investment. Current researches have focused on PSO and GA algorithms based on Markowitz's mean variance method. This paper examines the implementation of PSO and GA algorithms to solve the optimization problem of two portfolios, Borsa Istanbul (BIST) and Cryptocurrency Exchange (KPB). This study also uses Markowitz mean variance method, that is, variance method for risk minimization and mean method for profit maximization. PSO and GA varying coefficients are compared in terms of sharpe risk percentage and portfolio profit. The comparison result is in favor of PSO algorithm.

关键词： Portfolio optimization Level Variance Model Genetic algorithm particle swarm optimization algorithm

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