Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proxi...This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proximity to background structures.This method simulates the attention distribution mode of the human visual system which is used in Artificial Intelligence(AI)and called the Attention Mechanism.Based on the concept of static clutter filtering,the frequency-domain signals of the scanning aperture are divided into grid cells.Background scattering functions are established by analyzing the motion processes within each cell,and the background interference is linearly filtered out.An analysis of the manifestation of background scattering interference within the algorithm is carried out,and the impact of the grid cell dimension on the imaging quality is investigated.Experimental results show that the proposed method exhibits the capability to enhance the signal-to-noise ratio of both the target and the background.It effectively suppresses the background interference,leading to a more prominent image,meanwhile without imposing the excessive computational load.The method offers a novel solution for improving the performance of millimeter-wave imaging technology in practical applications.展开更多
Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous ...Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous WTA(HWTA)problem.Heterogeneous indicates that the engagement platforms carry multiple kinds of weapons for different tactical purposes.The targets assigned and the weapons used by one side’s platforms will affect the survival probability and capability of the other side’s platforms.The goal of each side in HWTA is to find a solution to determine the kind of weapon used and the target assigned for each platform,so as to maximize their combat effectiveness.The problem is formulated as a two-player noncooperative game model with considering the conflicts between the engaged sides.The Nash equilibrium is an effective solution to the game in which no player has an incentive to deviate.However,the number of pure strategies in HWTA increases exponentially with the engagement platforms.To improve computing efficiency,a double oracle algorithm with constructive heuristic(DOCH)is developed,within which the constructive heuristic is embedded to solve the oracle subproblems efficiently.Numerical experiments are conducted to verify the effectiveness of the DOCH.The results show that the DOCH can find effective strategies for platforms to improve combat effectiveness.Moreover,the DOCH can find high-quality solutions in seconds,significantly outperforming the state-of-the-art algorithms in terms of computational efficiency,especially for large-scale problems.展开更多
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e...Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.展开更多
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the...The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage.展开更多
SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的...SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的SWRS模型。最后,采用拟牛顿BFGS非线性寻优方法对新构建的SWRS模型进行寻优解算,设计了基于连续解析形式重力基准图的SITAN算法。在2′×2′卫星测高反演重力异常数据库的基础上设计了对比仿真实验。仿真实验结果表明,在INS指示航迹定位误差为9.764 n mile、航向误差为3°及重力量测均方根误差为3 mGal实验条件下,所设计的SITAN算法匹配定位精度为1.695 n mile,优于传统SITAN算法的定位精度2.827 n mile。展开更多
预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性...预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性自回归神经网络(nonlinear auto-regressive model with eXogenous inputs neural network,NARXNN)模型。首先,依据采集的动车组车轮磨耗3年连续实测数据,运用快速查找密度峰值聚类算法剔除异常数据,并利用二阶拉格朗日多项式插值处理缺失值,进行数据集预处理。然后,通过相空间重构将一维时间序列车轮直径测量数据扩展至多维,挖掘车轮磨耗参数的演变规律,采用主成分分析法提取影响动车组车轮直径参数指标变化的主要成分,构建了车轮磨耗预测模型的数据集样本。在此基础上,设计了一种带外源输入的非线性自回归神经网络NARXNN模型,采用列文伯格−马夸尔特算法(levenberg-marquardt,LM)优化NARXNN模型的权重和阈值,引入正交匹配追踪算法(orthogonal matching pursuit,OMP),以贪婪迭代的方式优化网络模型结构,在保证性能可靠性的同时删除冗余神经元,最终实现预测网络结构的精简。实验结果表明,LM-OMP算法优化的NARXNN神经网络模型结构简单、性能优越,能够快速稳定收敛。建立了未来2至6个月的车轮轮径磨耗预测模型,与其他经典预测模型对比结果表明,运用LM-OMP算法优化的NARXNN神经网络模型在动车组车轮磨耗预测方面具有更高的准确率和稳定性。运用该方法能够较精准地预测车轮磨耗的发展规律,可为动车组车轮的精准维修和科学使用提供参考。展开更多
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548,BK20250876)+2 种基金Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
文摘This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proximity to background structures.This method simulates the attention distribution mode of the human visual system which is used in Artificial Intelligence(AI)and called the Attention Mechanism.Based on the concept of static clutter filtering,the frequency-domain signals of the scanning aperture are divided into grid cells.Background scattering functions are established by analyzing the motion processes within each cell,and the background interference is linearly filtered out.An analysis of the manifestation of background scattering interference within the algorithm is carried out,and the impact of the grid cell dimension on the imaging quality is investigated.Experimental results show that the proposed method exhibits the capability to enhance the signal-to-noise ratio of both the target and the background.It effectively suppresses the background interference,leading to a more prominent image,meanwhile without imposing the excessive computational load.The method offers a novel solution for improving the performance of millimeter-wave imaging technology in practical applications.
基金supported by the National Natural Science Foundation of China(72571094,71871079,72271076,72001004)Anhui Provincial Natural Science Foundation(2308085QG233)+1 种基金Anhui Province Postdoctoral Research Activities Funds(2022B587)Talent Research Fund of Hefei University(24RC75).
文摘Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous WTA(HWTA)problem.Heterogeneous indicates that the engagement platforms carry multiple kinds of weapons for different tactical purposes.The targets assigned and the weapons used by one side’s platforms will affect the survival probability and capability of the other side’s platforms.The goal of each side in HWTA is to find a solution to determine the kind of weapon used and the target assigned for each platform,so as to maximize their combat effectiveness.The problem is formulated as a two-player noncooperative game model with considering the conflicts between the engaged sides.The Nash equilibrium is an effective solution to the game in which no player has an incentive to deviate.However,the number of pure strategies in HWTA increases exponentially with the engagement platforms.To improve computing efficiency,a double oracle algorithm with constructive heuristic(DOCH)is developed,within which the constructive heuristic is embedded to solve the oracle subproblems efficiently.Numerical experiments are conducted to verify the effectiveness of the DOCH.The results show that the DOCH can find effective strategies for platforms to improve combat effectiveness.Moreover,the DOCH can find high-quality solutions in seconds,significantly outperforming the state-of-the-art algorithms in terms of computational efficiency,especially for large-scale problems.
基金supported by the National Natural Science Foundation of China(61503408)。
文摘Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.
基金Projects(52374138,51764013)supported by the National Natural Science Foundation of ChinaProject(20204BCJ22005)supported by the Training Plan for Academic and Technical Leaders of Major Disciplines of Jiangxi Province,China+1 种基金Project(2019M652277)supported by the China Postdoctoral Science FoundationProject(20192ACBL21014)supported by the Natural Science Youth Foundation Key Projects of Jiangxi Province,China。
文摘The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage.
文摘SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的SWRS模型。最后,采用拟牛顿BFGS非线性寻优方法对新构建的SWRS模型进行寻优解算,设计了基于连续解析形式重力基准图的SITAN算法。在2′×2′卫星测高反演重力异常数据库的基础上设计了对比仿真实验。仿真实验结果表明,在INS指示航迹定位误差为9.764 n mile、航向误差为3°及重力量测均方根误差为3 mGal实验条件下,所设计的SITAN算法匹配定位精度为1.695 n mile,优于传统SITAN算法的定位精度2.827 n mile。
文摘预测车轮的磨耗状态对合理制定维护计划、保障动车组列车安全运行具有重要意义。动车组车轮磨耗属复杂非线性问题,引入机器学习和大数据技术对准确预测车轮磨耗具有显著作用。为预测动车组车轮磨耗,提出了一种改进的带外源输入的非线性自回归神经网络(nonlinear auto-regressive model with eXogenous inputs neural network,NARXNN)模型。首先,依据采集的动车组车轮磨耗3年连续实测数据,运用快速查找密度峰值聚类算法剔除异常数据,并利用二阶拉格朗日多项式插值处理缺失值,进行数据集预处理。然后,通过相空间重构将一维时间序列车轮直径测量数据扩展至多维,挖掘车轮磨耗参数的演变规律,采用主成分分析法提取影响动车组车轮直径参数指标变化的主要成分,构建了车轮磨耗预测模型的数据集样本。在此基础上,设计了一种带外源输入的非线性自回归神经网络NARXNN模型,采用列文伯格−马夸尔特算法(levenberg-marquardt,LM)优化NARXNN模型的权重和阈值,引入正交匹配追踪算法(orthogonal matching pursuit,OMP),以贪婪迭代的方式优化网络模型结构,在保证性能可靠性的同时删除冗余神经元,最终实现预测网络结构的精简。实验结果表明,LM-OMP算法优化的NARXNN神经网络模型结构简单、性能优越,能够快速稳定收敛。建立了未来2至6个月的车轮轮径磨耗预测模型,与其他经典预测模型对比结果表明,运用LM-OMP算法优化的NARXNN神经网络模型在动车组车轮磨耗预测方面具有更高的准确率和稳定性。运用该方法能够较精准地预测车轮磨耗的发展规律,可为动车组车轮的精准维修和科学使用提供参考。
