Some Comparison of Solutions by Different Numerical Techniques on Mathematical Biology Problem by Susmita Paul, Sankar Prasad Mondal, Paritosh Bhattacharya, Kripasindhu Chaudhuri

“…We try to compare the solutions by some numerical techniques when we apply the methods on some mathematical biology problems. The Runge-Kutta-Fehlberg (RKF) method is a promising method to give an approximate solution of nonlinear ordinary differential equation systems, such as a model for insect population, one-species Lotka-Volterra model. …”
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Generation of address sequences with specified switching activity and address repeatability by V. N. Yarmolik, N. A. Shevchenko, V. А. Levantsevich, D. V. Demenkovets

“…As an initial mathematical model, a modification of the indicated generation method proposed by Antonov and Saleev is used. …”
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Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM by Zhang Yuxue, Pan Hongxia

“…For the detection and identification problems of common faults in the use of gearbox,considering the nonlinear,non-stationary properties of the gearbox vibration response signal,a method for the gearbox fault diagnosis based on local mean decomposition（ LMD） approximate entropy and PSO-ELM is proposed.Firstly,the LMD decomposition method is used for gearbox vibration signal,with correlation coefficient method extracted the first four PF components which contain the main fault information. …”
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Application of improved P-BEM in time varying channel prediction in 5G high-speed mobile communication system by Hui Yuechao

“…To improve people’s communication experience on high-speed trains, this study proposed a time-varying channel prediction (TVCP) method based on an improved polynomial basis extension model and backpropagation neural network. …”
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A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…The proposed method, along with baseline model code and data, has been updated and is available at https://github.com/HaileiYuan/T2SNET-Pro.git.…”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…Compared with other methods, the proposed model exhibits superior stability and robustness.…”
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Solutions of Nonlinear Fractional-Order Differential Equation Systems Using a Numerical Technique by Mohammed Boukedroun, Souad Ayadi, Fouzia Chita, Meltem Erden Ege, Ozgur Ege, Rajagopalan Ramaswamy

“…Our proposed approach, the Multistage Telescoping Decomposition Elzaki Method (MTDEM), integrates the advantages of the Elzaki transform with the Multistage Telescoping Decomposition Method (MTDM), significantly enhancing the efficiency of the solution process and improving the convergence rate. …”
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Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing by Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, Tiancong Huang

“…This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. …”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. Traditional GNMF methods typically rely on predefined graph structures to guide the decomposition process, using fixed data graphs and feature graphs to capture relationships between data points and features. …”
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Photovoltaic output prediction based on VMD disturbance feature extraction and WaveNet by ShouSheng Zhao, Xiaofeng Yang, Kangyi Li, Xijuan Li, Weiwen Qi, Xingxing Huang

“…To address this, this paper proposes a PV output forecasting method based on Variational Mode Decomposition (VMD) disturbance feature extraction and the WaveNet model. …”
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Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism by Xuetao Jiang, Yuchun Gou, Meiyu Jiang, Lihui Luo, Qingguo Zhou

“…The proposed Conditional Decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. …”
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Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft by Ping-an Zhang, Wei Wang, Min Gao, Yi Wang

“…A novel H∞ filter called square-root cubature H∞ Kalman filter is proposed for attitude measurement of high-spinning aircraft. In this method, a combined measurement model of three-axis geomagnetic sensor and gyroscope is used, and the Euler angle algorithm model is used to reduce the state dimension and linearize the state equation, which can reduce the amount of calculation. …”
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A New Approach for Solving Nonlinear Differential Equations with Poincare Map and Poincare Section by Ghasem Sadeghi Bajestani, Ali Sheikhani, Seyed Mohammad Reza Hashemi Golpayegani, Farah Ashrafzadeh, Paria Hebrani

“…What is the acceptable replacement for the current methods? All the phenomena of the world have bifurcations, singularity, dissociation, behavioral changes and interaction; and today, science with assumptions like neglecting interactions and singularities, consider the systems as a continues model, although we are in need of a model in which we can solve the problem without inserting the  changes in time approach to zero chain. …”
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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine by Hong Yang, Lipeng Gao, Guohui Li

“…To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. …”
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Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem by Ludovica Adacher, Marco Tiriolo

“…This paper suggests the adoption of a spatial decomposition method to solve the signal synchronization problem. …”
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Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN by Wang Jinrui, Xie Lirong, Wang Zhongqiang, Niu Yongchao

“…According to the non-stationary and nonlinearity of the fault signals from rolling bearing,a recognition method based on Modified Ensemble Empirical Mode Decomposition（ MEEMD） and Double Hiddenlayer Elman Neural Network（ DHENN） is proposed. …”
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Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm by Jiaye Wang

“…In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. …”
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Deconvolution of optical spectra distorted by random noise using an artificial immune system by D.I. Galiakhmetova, M.E. Sibgatullin, D.Z. Galimullin, D.I. Kamalova

“…The results of the decomposition of the model contour consisting of three Gaussian components have been represented. …”
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Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience by Aastha M. Sathe, Supraja R., Aditya Antony Thomas

“…In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. …”
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Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer by Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang

“…The method integrates multi-frequency sequence feature analysis and multi-point correction using the FEDformer model. …”
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