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Propagation Alongside Crossover:An Evolutionary Algorithm for Continuous Optimization and Feature Selection 认领 引用
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作者 Najibeh Farzi-Veijouyeh Vahideh Sahargahi Neda Matin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1112-1175,共64页
Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(... Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(PAC)algorithm to address continuous optimization challenges.The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage.PAC simultaneously leverages the benefits of propagation,crossover,and mutation.Three independent operators are defined to generate new candidate solutions separately,and a novel selection strategy allows individuals produced by each operator,along with members of the current population,to independently enter the next generation.This design preserves population diversity,prevents all individuals from converging toward a single point,and enhances the algorithm’s ability to explore the solution space effectively.A key innovation of PAC is its three-mode propagation mechanism,which comprises local search,linear propagation toward the target point,and tear-drop shaped propagation toward the target point.Tear-drop propagation provides a precise and adaptive search around promising solutions,increasing diversity and preventing entrapment in local optima.The target point is typically set as the global optimum;however,when propagating the global optimum itself,a random point is used as the target to further enhance exploration and escape from local optima.The initial population is generated using chaotic mapping to ensure broad coverage of the search space.PAC was rigorously evaluated on 51 benchmark functions and three engineering problems,considering scalability,convergence,sensitivity,and computational efficiency.Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance,as confirmed by Wilcoxon signed-rank and Friedman statistical tests.Furthermore,PAC was applied as a feature selection method on four diverse datasets,achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy.These results highlight PAC’s versatility,robustness,and practical effectiveness. 展开更多
关键词 Optimization algorithms Meta-heuristic algorithms Continuous optimization Propagation alongside crossover algorithm Intrusion detection
Functional cartography of heterogeneous combat networks using operational chain-based label propagation algorithm 认领 引用
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作者 CHEN Kebin JIANG Xuping +2 位作者 ZENG Guangjun YANG Wenjing ZHENG Xue 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1202-1215,共14页
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra... To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning. 展开更多
关键词 functional cartography heterogeneous combat network functional module label propagation algorithm operational chain
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Space debris environment engineering model 2019:Algorithms improvement and comparison with ORDEM 3.1 and MASTER-8 认领 引用 被引量：2
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作者 Yuyan LIU Runqiang CHI +3 位作者 Baojun PANG HU Diqi Wuxiong CAO Dongfang WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第5期392-409,共18页
As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.I... As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J2)is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris. 展开更多
关键词 SDEEM2019 Space debris propagating algorithm Spatial density algorithm ORDEM 3.1 MASTER-8
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Wireless location algorithm using digital broadcasting signals based on neural network 认领 引用 被引量：1
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作者 柯炜 吴乐南 殷奎喜 《Journal of Southeast University(English Edition)》 EI CAS 2010年第3期394-398,共5页
In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. ... In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification. 展开更多
关键词 digital broadcasting signals neural network extended Kalman filter （EKF） backwards error propagation algorithm multilayer perceptron
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Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm 认领 引用
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作者 Sun Yushan Wang Jianguo +2 位作者 Wan Lei Hu Yunyan Jiang Chunmeng 《High Technology Letters》 EI CAS 2012年第3期243-247,共5页
Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the curr... Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision. 展开更多
关键词 underwater vehicle （UV） system identification neural network genetic algo-rithm （GA） back propagation algorithm
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Track Association for Dynamic Target Tracking System Based on AP Algorithm 认领 引用
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作者 储岳中 徐波 高有涛 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第6期643-651,共9页
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.... Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association. 展开更多
关键词 affinity propagation algorithm data fusion target tracking track association
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Age and Gender Classification Using Backpropagation and Bagging Algorithms 认领 引用
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作者 Ammar Almomani Mohammed Alweshah +6 位作者 Waleed Alomoush Mohammad Alauthman Aseel Jabai Anwar Abbass Ghufran Hamad Meral Abdalla Brij B.Gupta 《Computers, Materials & Continua》 SCIE EI 2023年第2期3045-3062,共18页
Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and ... Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%. 展开更多
关键词 Classify voice gender accent age bagging algorithms back propagation algorithms AI classifiers
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Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network 认领 引用
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作者 Li LIU Qingfeng XU +3 位作者 Hui GU Lei ZHOU Zhenfu LIU Lili CAO 《Journal of Geodesy and Geoinformation Science》 CSCD 2021年第4期74-83,共10页
The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation... The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data. 展开更多
关键词 modular feedforward neural network aeromagnetic compensation LM back propagation algorithm
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A Third-Order Accurate Wave Propagation Algorithm for Hyperbolic Partial Differential Equations 认领 引用
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作者 Christiane Helzel 《Communications on Applied Mathematics and Computation》 EI 2020年第3期403-427,共25页
We extend LeVeque's wave propagation algorithm,a widely used finite volume method for hyperbolic partial differential equations,to a third-order accurate method.The resulting scheme shares main properties with the... We extend LeVeque's wave propagation algorithm,a widely used finite volume method for hyperbolic partial differential equations,to a third-order accurate method.The resulting scheme shares main properties with the original method,i.e.,it is based on a wave decomposition at grid cell interfaces,it can be used to approximate hyperbolic problems in divergence form as well as in quasilinear form and limiting is introduced in the form of a wave limiter. 展开更多
关键词 Wave propagation algorithm Hyperbolic partial differential equations Third-order accuracy
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Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm 认领 引用
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作者 颜红梅 《Journal of Chongqing University》 2003年第1期31-34,共4页
A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale ... A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis. 展开更多
关键词 multilayer perceptron back propagation algorithm heart disease momentum term adaptive learning rate the forgetting mechanics conjugate gradients method
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Index Modulation Aided OTFS for SCMA System 认领 引用
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作者 He Chunlin Xiao Lixia +3 位作者 Li Shuo Liu Weidan Xiao Pei Jiang Tao 《China Communications》 SCIE EI CSCD 2025年第8期19-28,共10页
In this paper,an index modulation(IM)aided uplink orthogonal time frequency space modulation(OTFS)structure for sparse code multiple access(SCMA)is proposed.To be more specific,the information bits are firstly partiti... In this paper,an index modulation(IM)aided uplink orthogonal time frequency space modulation(OTFS)structure for sparse code multiple access(SCMA)is proposed.To be more specific,the information bits are firstly partitioned for transmit antenna(TA)selection and sparse codeword mapping,respectively.Subsequently,the codewords deployed on the 2-dimensional(2D)delay-Doppler(DD)plane are transmitted by the selected TA,and the superimposed signals are jointly detected at the receiver.Furthermore,a low-complexity zero-embedded expectation propagation(ZE-EP)detector is conceived,where the codebooks are extended with zero vectors to reflect the silent indices.The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart. 展开更多
关键词 expectation propagation algorithm index modulation message passing algorithm OTFS SCMA
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Rail profile optimization through balancing of wear and fatigue 认领 引用 被引量：1
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作者 Binjie XU Zhiyong SHI +2 位作者 Yun YANG Jianxi WANG Kaiyun WANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第10期967-982,共16页
Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focus... Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model. 展开更多
关键词 Heavy-haul railway Rail wear Rail fatigue Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network) Rail profile optimization Multi-objective optimization
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Models for Predicting the Minimum Miscibility Pressure(MMP)of CO2-Oil in Ultra-Deep Oil Reservoirs Based on Machine Learning 认领 引用
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作者 Kun Li Tianfu Li +5 位作者 Xiuwei Wang Qingchun Meng Zhenjie Wang Jinyang Luo Zhaohui Wang Yuedong Yao 《Energy Engineering》 EI 2025年第6期2215-2238,共24页
CO2flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO2and ... CO2flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO2and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO2-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO2-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(TR)exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C2-C4,CO2,and H2S)exhibits the least influence. 展开更多
关键词 MMP random forest regression genetic algorithm based back propagation artificial neural network support vector regression gaussian process regression
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Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders 认领 引用 被引量：7
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作者 Danjie Sheng Jin Yu +3 位作者 Fei Tan Defu Tong Tianjun Yan Jiahe Lv 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第7期1749-1758,共10页
Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep... Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineersesearchers interested in analyzing rock mass classification criteria or performing field investigation. 展开更多
关键词 Rock mass quality classification Deep learning Stacked autoencoder(SAE) Back propagation algorithm
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Detecting community structure using label propagation with consensus weight in complex network 认领 引用 被引量：9
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作者 梁宗文 李建平 +1 位作者 杨帆 Athina Petropulu 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第9期594-601,共8页
Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community struc... Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight （LPAcw）, and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions. 展开更多
关键词 label propagation algorithm community detection consensus cluster complex network
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks 认领 引用 被引量：2
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作者 Fei Li Jiayan Zhang +3 位作者 Edward Szczerbicki Jiaqi Song Ruxiang Li Renhong Diao 《Computers, Materials & Continua》 SCIE EI 2020年第10期653-681,共29页
The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these... The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate. 展开更多
关键词 Internet of vehicles safety protection technology intrusion detection system advanced auto-encoder recurrent neural network time-based back propagation algorithm
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Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China 认领 引用 被引量：8
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作者 ZHU Lin GONG Huili +3 位作者 LI Xiaojuan LI Yongyong SU Xiaosi GUO Gaoxuan 《Chinese Geographical Science》 SCIE CSCD 2013年第2期237-248,共12页
Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, incl... Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy. 展开更多
关键词 land subsidence groundwater level change compressible sediments thickness building area Back Propagation NeuralNetwork and Genetic Algorithm （BPN-GA） model
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Artificial neural network approach to assess selective flocculation on hematite and kaolinite 认领 引用 被引量：2
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作者 Lopamudra Panda P.K.Banerjee +2 位作者 Surendra Kumar Biswal R.Venugopal N.R.Mandre 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2014年第7期637-646,共10页
Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alt... Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values. 展开更多
关键词 hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency
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Performance prediction of gravity concentrator by using artificial neural network-a case study 认领 引用 被引量：5
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作者 Panda Lopamudra Tripathy Sunil Kumar 《International Journal of Mining Science and Technology》 EI CAS 2014年第4期461-465,共5页
In conventional chromite beneficiation plant,huge quantity of chromite is used to loss in the form of tailing.For recovery these valuable mineral,a gravity concentrator viz.wet shaking table was used.Optimisation alon... In conventional chromite beneficiation plant,huge quantity of chromite is used to loss in the form of tailing.For recovery these valuable mineral,a gravity concentrator viz.wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So,in this present study,an artificial neural network(ANN)modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%)and recovery(%).A three layer feed forward neural network(3:3–11–2:2)was developed by varying the major operating parameters such as wash water flow rate(L/min),deck tilt angle(degree)and slurry feed rate(L/h).The predicted value obtained by the neural network model shows excellent agreement with the experimental values. 展开更多
关键词 Chromite Artificial neural network Wet shaking table Performance prediction Back propagation algorithm
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Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment 认领 引用 被引量：5
20
作者 B.K.Sahoo S.De B.C.Meikap 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2017年第2期379-386,共8页
Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheol... Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model. 展开更多
关键词 Microwave pre-treatment Coal-water slurry Apparent viscosity Artificial neural network Back propagation algorithm
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