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Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm 认领 引用 被引量：1
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作者 Chengwen Wang Xiaoli Liu +4 位作者 Jiubao Li Enzhi Wang Nan Hu Wenli Yao Zhihui He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7038-7055,共18页
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache... Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations. 展开更多
关键词 Back analysis of rock parameters Auto machine learning Multi-objective optimization algorithm Mechanized twin tunnels Parametric modeling
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Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces 认领 引用 被引量：2
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作者 Wenhao Yi Mingnian Wang +2 位作者 Qinyong Xia Yongyi He Hongqiang Sun 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第1期258-274,共17页
The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To... The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests. 展开更多
关键词 Rockburst prediction Drilling parameters Feature system Extreme tree(ET) Bayesian optimization
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Improved sparrow search algorithm for inversion of geometric parameters of earthquake source faults 认领 引用
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作者 Leyang Wang Xuekai Zhou +2 位作者 Zhanglin Sun Can Xi Hao Xiao 《Geodesy and Geodynamics》 EI CSCD 2025年第6期665-680,共16页
With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Spar... With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Sparrow Search Algorithm(SSA)is proposed for the seismic source parameter inversion problem.By replacing the original population generation in the improved algorithm with Latin hypercubic sampling,the Sparrow Search Algorithm reduces the repetition of samples in the population initialization.Subsequently,the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process.Therefore,the improved Latin hypercube-based sparrow search algorithm(ILHSSA)has better advantages in terms of iterative convergence speed and stability.In order to verify the performance of ILHSSA,the basic genetic algorithm(GA)and sparrow search algorithm(SSA)are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types.The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability.Compared with the GA algorithm,ILHSSA can achieve the same inversion accuracy as GA,and it even surpasses GA in inversion speed and the inversion results of some parameters,demonstrating better stability.Finally,the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy.The inversion results all reflect the practicality and reliability of the improved algorithm. 展开更多
关键词 Sparrow search algorithm Latin hypercube Source parameter inversion Bodrum-Coase earthquake Amatrice earthquake
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GBO algorithm for seismic source parameters inversion 认领 引用 被引量：3
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作者 Leyang Wang Han Li 《Geodesy and Geodynamics》 EI CSCD 2023年第2期182-190,共9页
The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault par... The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer(GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization(MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion. 展开更多
关键词 Fault source parameters inversion Gradient-based optimizer algorithm Nonlinear Multi-peak particle swarm optimization algorithm GPS data
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Optimization of Operating Parameters for Underground Gas Storage Based on Genetic Algorithm 认领 引用
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作者 Yuming Luo Wei Zhang +7 位作者 Anqi Zhao Ling Gou Li Chen Yaling Yang Xiaoping Wang Shichang Liu Huiqing Qi Shilai Hu 《Energy Engineering》 EI 2025年第8期3201-3221,共21页
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr... This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×108 m3,which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability. 展开更多
关键词 Underground gas storage operational parameter optimization extreme peak-shaving constraints genetic algorithm model
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Bayesian AVO inversion of fluid and anisotropy parameters in VTI media using IADR-Gibbs algorithm 认领 引用
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作者 Ying-Hao Zuo Zhao-Yun Zong +3 位作者 Xing-Yao Yin Kun Li Ya-Ming Yang Si Wu 《Petroleum Science》 SCIE EI CAS CSCD 2025年第9期3565-3582,共18页
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit... Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications. 展开更多
关键词 Fluid and anisotropy parameters AVO inversion Bayesian framework Probabilistic inversion
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Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm 认领 引用
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作者 Bo Yang Ruyi Zheng +2 位作者 Yucun Qian Boxiao Liang Jingbo Wang 《Global Energy Interconnection》 EI CSCD 2025年第2期316-337,共22页
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a... Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively. 展开更多
关键词 Photovoltaic cell Bayesian neural network Artificial ecosystem optimization Parameter identification
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Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm 认领 引用 被引量：18
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作者 Hong-li QI Hui ZHAO +1 位作者 Wei-wen LIU Hai-bo ZHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第8期1205-1212,共8页
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa... A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS. 展开更多
关键词 Grating eddy current displacement sensor （GECDS） Artificial neural network （ANN） Genetic algorithm （GA） Parameters optimization Nonlinearity error
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Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology 认领 引用 被引量：4
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作者 Houfa Wu Jianyun Zhang +4 位作者 Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang 《Engineering》 SCIE EI CAS CSCD 2023年第9期93-104,共12页
Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization... Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data. 展开更多
关键词 Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and water assessment tool model
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Inverting reservoir parameters in a two-phase fractured medium with a niche genetic algorithm 认领 引用 被引量：3
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作者 张生强 韩立国 +2 位作者 刘春成 张益明 巩向博 《Applied Geophysics》 SCIE CSCD 2012年第4期440-450,496,共11页
In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversio... In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversion problem of the wave equation in a two-phase medium. We propose a niche genetic multi-parameter （including porosity, solid phase density and fluid phase density） joint inversion algorithm based on a two-phase fractured medium in the BISQ model. We take the two-phase fractured medium of the BISQ model in a two- dimensional half space as an example, and carry out the numerical reservoir parameters inversion. Results show that this method is very convenient for solving the parameters inversion problem for the wave equation in a two-phase medium, and has the advantage of strong noise rejection. Relative to conventional genetic algorithms, the niche genetic algorithm based on a sharing function can not only significantly speed up the convergence, but also improve the inversion precision. 展开更多
关键词 Parameters inversion niche genetic algorithm BISQ model two-phase fracturedmedium wave equation
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Research on the Optimum Configuration Strategy for the Adjustable Parameters in Ant Colony Algorithm 认领 引用 被引量：16
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作者 Haibin Duan Daobo Wang Xiufen Yu 《通讯和计算机（中英文版）》 2005年第9期32-35,共4页
In order to investigate the relative strength and weaknesses of the optimum configuration strategy for the adjustable parameters in the Ant Colony Algorithm （ACA）, series of experiments are conducted in this paper. ... In order to investigate the relative strength and weaknesses of the optimum configuration strategy for the adjustable parameters in the Ant Colony Algorithm （ACA）, series of experiments are conducted in this paper. On the basis of the experimental results presented above, an effective ＂three-step＂ optimum configuration strategy for the adjustable parameters in ACA is drawn. 展开更多
关键词 Ant Colony Algorithm （ACA） Pheromone Optimum Configuration Three-step Adjustable Parameters
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Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm 认领 引用
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作者 Jiang Li Jiutao Zhao +3 位作者 Qinhui Liu Laizheng Zhu Jinyi Guo Weijiu Zhang 《Computers, Materials & Continua》 SCIE EI 2023年第10期223-244,共22页
Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImpr... Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters. 展开更多
关键词 Machining parameters Bp neural network Multiple Objective Particle Swarm Optimization Bp-DWMOPSO algorithm
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A new calibration algorithms of spinning projectile aerodynamic parameters 认领 引用
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作者 丛明煜 张伟 王丽萍 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第4期440-445,共6页
This paper demonstrates that the application of calibration algorithms of aerodynamic parameters for the trajectory of spinning projectile is successful. First, from the point of view of the trajectory simulation, a g... This paper demonstrates that the application of calibration algorithms of aerodynamic parameters for the trajectory of spinning projectile is successful. First, from the point of view of the trajectory simulation, a general summary of well-known trajectory models is given. A five degrees of freedom （5 DOF） model is developed that can match the projectile motion essentially in the vertex region, and the results obtained by 5 DOF model are in close agreement with those of a more sophisticated 6 DOF model for elevation angles above 45 degrees. Secondly, the calibration algorithms have been developed and are summarized. The methods of calibrating the flight trajectory models are compared, and these methods are shown to be effective in the representative cases. In addition, the method of Math number calibration （MNC） is presented; some possible areas in MNC for further investigation are indicated together with benefits to be gained. The utilization of MNC schemes not only allow a worthwhile reduction of calibration rounds firing in range and accuracy （R＆A） trial and production of firing tables （PFT） test, but also make PFT and fire control data （FCD） more cost effective. 展开更多
关键词 trajectory models trajectory simulation spinning projectile aerodynamic parameters calibration algorithms
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A genetic algorithm for dynamic parameters reverse deduction of integrated anchorage system 认领 引用
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作者 张永兴 《Journal of Chongqing University》 2006年第1期46-49,共4页
In the analysis of the system of anchoring bar and wall rock in small strain and longitudinal vibration dynamic response, the influence of the cement grouting as well as the rock layer on the anchor bar can be evaluat... In the analysis of the system of anchoring bar and wall rock in small strain and longitudinal vibration dynamic response, the influence of the cement grouting as well as the rock layer on the anchor bar can be evaluated as the two kinds of parameters: the dynamic stiffness and the damp, which are the vital reference of the anchorage quality. Based on the analytic solution to the dynamic equation of the integrated anchor bar, the new approach which combines genetic algorithm and the toolbox of Matlab is applied to solve the problem of multi-parameters reverse deduction for integrated anchorage system in dynamic testing. Using the traits of the self-organizing, self-adapting and the fast convergence speed of the genetic algorithm, the optimum of all possible solutions to dynamic parameters is obtained by calculating the project instances. Examples show that the method presented in this paper is effective and reliable. 展开更多
关键词 integrated anchorage dynamic parameters reverse deduction genetic algorithm
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Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms 认领 引用 被引量：4
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作者 Junsheng Zhao Yuantong Gu Zhigang Feng 《Journal of Beijing Institute of Technology》 EI CAS 2019年第3期606-616,共11页
A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization o... A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications. 展开更多
关键词 power spinning process parameters optimization BP neural network genetic algorithms (GA) response surface methodology (RSM)
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Optimizing the neural network hyperparameters utilizing genetic algorithm 认领 引用 被引量：23
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作者 Saeid NIKBAKHT Cosmin ANITESCU Timon RABCZUK 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2021年第6期407-426,共20页
Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several problems.However,choosing proper hyperparameters(e.g.the numbers of layers and neurons i... Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several problems.However,choosing proper hyperparameters(e.g.the numbers of layers and neurons in each layer)has a significant influence on the accuracy of these methods.Therefore,a considerable number of studies have been carried out to optimize the NN hyperpaxameters.In this study,the genetic algorithm is applied to NN to find the optimal hyperpaxameters.Thus,the deep energy method,which contains a deep neural network,is applied first on a Timoshenko beam and a plate with a hole.Subsequently,the numbers of hidden layers,integration points,and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures.Thus,applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples. 展开更多
关键词 Machine learning Neural network(NN) Hyperparameters Genetic algorithm
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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm 认领 引用 被引量：16
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作者 毛勇 周晓波 +2 位作者 皮道映 孙优贤 WONG Stephen T.C. 《Journal of Zhejiang University-SCIENCE B》 EI CAS 2005年第10期961-973,共13页
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result... In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes. 展开更多
关键词 Gene selection, Support vector machine (SVM), Recursive feature elimination (RFE), Genetic algorithm (GA),Parameter selection
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Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm 认领 引用 被引量：5
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作者 夏毅敏 张刚强 +2 位作者 聂四军 卜英勇 张振华 《Journal of Central South University》 SCIE EI CAS 2011年第3期650-657,共8页
Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting hea... Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn. 展开更多
关键词 cobalt crust mining parameter specific energy consumption simulated annealing genetic algorithm
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Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm 认领 引用 被引量：2
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作者 Yan Shi Jiange Kou +2 位作者 Zhenlei Chen Yixuan Wang Qing Guo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第2期100-114,共15页
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i... Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value. 展开更多
关键词 Parameter identification Enhanced whale optimization algorithm(EWOA) Backstepping Human-robot interaction Lower limb exoskeleton
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Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller 认领 引用 被引量：11
20
作者 LIU Lu SHAN Liang +2 位作者 DAI Yuewei LIU Chenglin QI Zhidong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第1期166-175,共10页
The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is... The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system. 展开更多
关键词 bacterial foraging algorithm fractional-order quantum rotation gate proportion integration differentiation(PID) servo system
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