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An Adaptive neural network algorithm with Quasi Opposition-Based Learning for Numerical Optimization Problems

COGNITIVE COMPUTATION 2025年第1期17卷 1-29页

作者： Kundu, Tanmay Garg, Harish Saveetha Inst Med & Tech Sci Saveetha Med Coll & Hosp Ctr Global Hlth Res Chennai 602105 India Sambhu Nath Coll Labpur Dept Math Birbhum 731303 West Bengal India Thapar Inst Engn & Technol Dept Math Patiala 147004 Punjab India

The structure of artificial neural networks and the biological nervous systems serve as the foundation for the creation of the neural network algorithm (NNA). The robust global search capability of NNAs makes it an effective tool for solving a wide range of complex optimization problems. Unfortunately, its limited relevance to many optimization problems is due to its poor exploitation, weak convergence, and tendency to fall into local optima. The paper's goal is to introduce an enhanced version of the NNA known as the adaptive quasi-opposition-based neural network algorithm (AQOBNNA) in order to overcome these issues. The quasi-opposition-based learning (QOBL) and an adaptive strategy are combined in this suggested algorithm, where the adaptive technique is added to determine whether or not to use QOBL. The QOBL technique replaces a random search individual with the best one throughout the position update phase in order to enhance exploitation and increase exploration capabilities. The performance of the suggested AQOBNNA is assessed using a set of 23 traditional benchmark functions and compared with a number of current methods. It is evident from the experimental data that AQOBNNA performs better overall and outperforms all the algorithms that were examined.

关键词： neural network algorithm Constrained optimization Quasi-opposition-based learning Benchmark CEC test functions

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Thermal radiation image based on neural network algorithm for muscle balance simulation during exercise training: Thermal conduction of exercise cells

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 57卷

作者： Chai, Zhiheng Xian Peihua Univ Sch Phys Educ & Sports Xian 710125 Shaanxi Peoples R China

In modern sports training, muscles generate heat during activity, and this heat conduction process has an important impact on muscle function and overall athletic performance. In this study, the neural network algorithm is used to analyze the thermal radiation image to achieve accurate simulation of muscle balance in sports training. A multi-layer neural network model is constructed by collecting a large amount of infrared thermal image data during exercise training. By dividing the data set into a training set and a test set, we train and validate the neural network model. In the course of training, the weight of the model is constantly adjusted by backpropagation algorithm to improve the accuracy of prediction. After the model training is completed, we use the model to simulate and analyze the new thermal image data to evaluate the heat conduction behavior of muscles under different motion states. The results show that the thermal radiation image analysis method based on neural network algorithm can effectively monitor and analyze the heat conduction behavior of moving cells. Through infrared thermal image monitoring, we can clearly see the temperature changes of muscles during exercise, and the relationship between these changes and muscle function. The prediction results of the neural network model are highly consistent with the actual measurement data, which verifies the accuracy and reliability of the method. This method can not only monitor the heat conduction process of the motor cells in real time, but also provide scientific data support for the exercise training.

关键词： neural network algorithm Thermal radiation image Sports training Muscle balance simulation Motor cells Heat conduction

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Social entertainment robot based on neural network algorithm in personalized music course simulation

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Gao, Bo Li, Qianni Hunan Normal Univ Sch Mus Changsha 410012 Peoples R China

This study focuses on the simulation of personalized music courses using social entertainment robots based on neural network algorithms, and introduces the current research status of music processing algorithms based on neural networks. A detailed explanation was provided on the training methods for neural network algorithms, including BP neural network training and music generation algorithms. Research was conducted on activation functions, music output detection, and loss function detection. A virtual music course system framework has been designed to provide students with personalized learning experiences. In a social entertainment robot based on neural network algorithms, a complete control node system was reconstructed, aiming to achieve interactive and entertainment effects of music courses through sensing technology. The control node system includes multiple key components, and the robot is able to perceive and recognize the actions, facial expressions, and sounds of students. Social entertainment robots have intelligent classification and recommendation functions, and this algorithm can intelligently classify and recommend music resources based on students' music preferences, levels, and needs. By analyzing the personal preferences and learning progress of students, robots can provide customized music course content and learning materials for each student, improving learning effectiveness and personal interests.

关键词： neural network algorithm Social entertainment robots Music courses Personalized courses

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Settlement prediction of a high embankment based on non-linear regression and neural network algorithm

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TRANSPORTATION GEOTECHNICS 2025年 50卷

作者： Yang, Enhui Wang, Kai He, Jinhong Liu, Kaiwen Wang, Junxin Zhang, Haopeng Qiu, Yanjun Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Sichuan Zhenguang Expressway CO LTD Chengdu 610041 Peoples R China Highway Engn Lab Sichuan Prov Chengdu 610031 Sichuan Peoples R China Minist Educ Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China

This study reports in-situ measured data from single-point settlement meters buried in layers at the road shoulders and the center of a high embankment respectively. Based on the measured data, the nonlinear fitting models and neural network algorithm were proposed to establish the method of settlement prediction, and the mean absolute percentage error (MAPE) and mean square errors (MSE) were used to evaluate the accuracy of the settlement prediction model. The results show that the MAPE value of the exponential curve prediction model is less than 10%, and the MAPE value of the hyperbolic model is between 10% and 20%, and the MAPE value of the logarithmic model is greater than 20%, which shows that the exponential curve model of the three nonlinear fitting models has the highest accuracy. After establishing the back propagation (BP) neural network model, the settlement data of the four monitoring points were learned and trained, and the prediction accuracy of the two BP neural network prediction models and the exponential curve prediction model were compared. The model fitting coefficient of R 2 of BP neural network were both greater than 0.99, and the MSE and MAPE were less than 1%. In addition, the multi-step rolling BP neural network prediction model has the highest prediction accuracy, followed by the BP neural network prediction model based on influencing factors while the exponential curve prediction model has the worst performance and weak practicability. This research can provide new inspiration for embankment settlement prediction and give technical support to monitor the disaster of high embankment.

关键词： High embankment Settlement prediction On-site measurement Non-linear regression neural network algorithm

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Optimizing Neutrino Mass Predictions with neural network algorithm and SU(2)L×U(1)Y×T7×Z10 Symmetry

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International Journal of Theoretical Physics 2025年第5期64卷 1-18页

作者： Aslam, Muhammad Waheed Zafar, Abrar Ahmad Department of Physics University of the Punjab Lahore Pakistan

This research aims to address the limitations of the Standard Model (SM), which assumes massless neutrinos, by developing a model incorporating SU(2)L×U(1)Y×T7×Z10 symmetry. Using a hybrid type I and type II seesaw mechanism, the model predicts neutrino masses and mixing parameters. Using neural network algorithm (NNA), the study calculates neutrino mass eigenvalues as: m1=21.5411meV,m2=23.2147meV,m3=55.2442meV (m1=49.3498meV,m2=50.1029meV,m3=0.547342meV), the matrix UPMNS and the effective neutrino mass parameters as: mβ=23.3706meV, mee=22.7984meV (mβ=49.0997meV, mee=48.6039meV) for normal (inverted) mass hierarchy. The mixing angles (θab, with a

关键词： Discrete symmetry neural network algorithm Neutrino mixing

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Immersive e-learning application in intelligent teaching of English composition based on neural network algorithm

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Zhao, Bing Pan, Deng Huzhou Coll Sch Humanities Huzhou 313000 Peoples R China Philippine Christian Univ Ctr Int Educ Manila 1004 Philippines Hubei Univ Sci & Technol Sch Foreign Languages Xianning 437100 Peoples R China

Technical solutions for evaluating traditional English teaching composition and other problems, such as excessive subjectivity, waste of time, slow feedback, short scoring time, heavy tasks and so on. This paper first analyzes the theoretical basis of neural network algorithm, and completes the preprocessing of the composition text submitted by students from three aspects: text segmentation, text representation and text fragment retrieval. Then, an intelligent evaluation system of English composition is established, and the scoring standard of English composition is determined. On this basis, the design of English scoring module is improved, and an anti cheating module for common plagiarism problems in English composition is added. Finally, the English composition intelligent evaluation system is tested. It can effectively complete the target task, evaluate and correct the composition, and has a higher recognition success rate and accuracy rate. By studying the algorithm and applying it to the construction of English composition intelligent evaluation system, this paper successfully designs a kind of English composition intelligent evaluation system, which promotes the development of teaching work.

关键词： neural network algorithm English teaching Composition practice Intelligent evaluation

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Fractional Order PID Controller Based-neural network algorithm for LFC in Multi-Area Power Systems

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第2期7卷

作者： El-Rifaie, Ali M. Abid, Slim Ginidi, Ahmed R. Shaheen, Abdullah M. Amer Univ Middle East Coll Engn & Technol Egaila Kuwait Jazan Univ Coll Engn & Comp Sci Dept Elect & Elect Engn Jazan Saudi Arabia ENIS Ecole Natl Ingn Sfax Sfax Tunisia Suez Univ Fac Engn Dept Elect Engn Suez Egypt

Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in contemporary multi-area interconnected power systems (MAIPS). This paper introduces an innovative approach to Load Frequency Control (LFC) using a Fractional-Order PID (FOPID) controller, optimized by a neural network algorithm (NNA). The proposed NNA-FOPID framework leverages the biological principles of neural networks to dynamically tune controller parameters, significantly enhancing system performance. The solution is tested under various scenarios involving step load changes across multi-area systems. The proposed method demonstrates marked improvements over traditional PID controllers and advanced optimization techniques such as Differential Evolution (DE) and Artificial Rabbits algorithm (ARA). The comparisons show that the FOPID controller's NNA-based design effectively and successfully handles LFC in MAIPSs for ITAE minimizations, and statistical evaluation supports its superiority.

关键词： fractional-order PID controller grid stability multi-area power systems neural network algorithm optimization techniques

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The Application of neural network algorithm in Computer Mathematical Modeling

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Procedia Computer Science 2025年 259卷 623-630页

作者： Wenying Zhao Xiaohong Li Mingjie Shi Weifang Engineering Vocational College Weifang 262500 Shandong China Weichai Ballard Hydrogen Energy Technology Co. Ltd. Weifang 261000 Shandong China

The application research of neural network algorithm in computer mathematical modeling has made extensive development. With its strong learning and approximation ability, it has shown great potential and application prospect in many fields. Through literature review and comparative analysis, this study compared the performance of CNN and RNN in the mathematical model of financial risk. The two algorithms have different performances under the same mathematical model. The results of the study showed that the accuracy of risk assessment of CNNS was between 93% and 98%, while the accuracy of RNN was between 89% and 96%, and the performance of CNNS was between 3-6s and RNN was between 4-8s on the assessment time. For the same neural network algorithm model, the two algorithms show different performance in financial risk assessment, because the weight parameter sharing in CNN can significantly reduce the number of parameters in the model, thus reducing the risk of overfitting.

关键词： computer mathematical modeling neural network algorithm financial risk assessment model convergence of algorithm

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Research on Energy Efficiency Improvement Strategy of Air Conditioning in Comprehensive Energy System Based on neural network algorithm

Research on Energy Efficiency Improvement Strategy of Air Co...

引用

2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Shan, Miao Liang, Jie Chai, Chao Jia, Shaokun Beijing Fibrlink Communications Co. LTD. Beijing China

ISBN: (纸本)9798331506797

To enhance the precision of the integrated energy system's operational approach, the study suggests utilizing a BP neural network for forecasting thermal and cooling demands, thereby minimizing forecast inaccuracies. This paper develops models for ice storage, gas boilers, and ground source heat pump cooling and heating equipment outputs. To enhance system efficiency, an energy efficiency target function is established. Simulation analysis shows that the BP neural network reduces prediction errors more effectively than exponential smoothing and gray prediction methods. The optimized operation model for the integrated energy system boosts the air conditioning system's comprehensive energy efficiency from 3.2 to 3.7. © 2025 IEEE.

关键词： Efficiency improvement Integrated energy system neural network algorithm

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neural network algorithm with transfer learning and dropout for using a UAV to search the lost target in motion

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Zhang, Yiying Zhou, Guanzhong Huang, Chao Huang, Hailong Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Kowloon Hong Kong Peoples R China

When performing the rescue mission, how to find a lost target in motion as soon as possible is a very valuable and interesting research topic. To find the optimal flight path of the UAV used to search the lost target in motion under different prior knowledge and interference intensity, this paper proposes a novel optimization technique called neural network algorithm with transfer learning and dropout (TLDNNA), which is a variant of neural network algorithm (NNA) inspired by artificial neural networks. To improve the global search ability and convergence performance of NNA, the multiple transfer mechanism, the generalized mean transfer position, and the random dropout operator driven by the principles of transfer learning and dropout are introduced to TLDNNA. To investigate the performance of TLDNNA, TLDNNA and 12 powerful population-based optimization algorithms are applied to the problem of using a UAV to search the lost target in motion in the designed 9 scenarios. Experimental results show that TLDNNA is remarkably better than the other 12 algorithms inmost of the scenarios in terms of solution quality, stability, and convergence performance. In addition, TLDNNA can improve the overall search performance by 18.14% and overall computational efficiency by 3.41 times compared to NNA. The source code of TLDNNA can be obtained by https://***/jsuzyy/TLDNNA.

关键词： neural network algorithm Dropout Unmanned aerial vehicles Target searching Metaheuristics

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