期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到5,951篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A back-propagation neural network optimized by genetic algorithm for rock joint roughness evaluation 认领 引用 被引量：1
1
作者 Leibo Song Jieru Xie +4 位作者 Quan Jiang Gang Wang Shan Zhong Guansheng Han Jinzhong Wu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第5期3054-3072,共19页
The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optim... The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints. 展开更多
关键词 Rock joint Joint roughness coefficient Genetic algorithm-optimized backpropagation(GA-BP)neural network Shear strength
暂未订购 下载PDF
Ship Magnetic Field Modeling and Extrapolation Based on a Convolutional Neural Network 认领 引用
2
作者 Ao Zhou Yadong Zhang +3 位作者 Wentie Yang Zuoshuai Wang Jianxun Wang Zhiwei Chen 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期536-549,共14页
Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields base... Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields based on genetic algorithms and convolutional neural networks(CNNs).The magnetic probe position matrix of the traditional equivalent source is utilized as input,and the three-directional components of the magnetic field measured by the probes are employed as output.The extrapolation model for ship magnetic fields is obtained through iterative training and fitting with CNNs.Variables such as the number of magnetic dipoles,the distance between magnetic dipoles,the size and quantity of convolutional kernels,batch size,learning rate,and L2 regularization coefficient are optimized to boost the accuracy of the extrapolation model for magnetic fields.The fitting accuracy of the extrapolation model for ship magnetic fields is used as the optimization objective.Based on a finite element simulation model of ship magnetic fields,the accuracy and robustness of the CNN algorithm under different magnetic field conditions are validated using the known standard depth plane,the unknown depth at 1.125 times the standard depth plane,and the unknown depth at 1.25 times the standard depth plane.Results show that,after optimization,the fitting error for the magnetic field extrapolation model based on CNN is 1.50%for the standard depth plane,1.63%for the unknown depth at 1.125 times the standard depth plane,and 2.36%for the unknown depth at 1.25 times the standard depth plane.The error remains below 5%under varying magnetic field conditions.When a random measurement error of 0%-5%is introduced for the magnetic probes,the prediction error at 1.25 times the standard depth plane is 2.30%;with a random error of 0%-10%,the prediction error is 4.95%.This approach significantly improves the accuracy and robustness of magnetic field extrapolation,which makes it an effective and feasible method for ship magnetic field modeling. 展开更多
关键词 Shipboard magnetic field Convolutional neural network Genetic algorithm Equivalent source method Magnetic field extrapolation
暂未订购 下载PDF
Efficient programming of a large-scale optical neural network with linear disordered media 认领 引用
3
作者 Kun Jin Yi Zhou +5 位作者 Jun Li Yue Tao Tao Wang Can Li Jinyong Leng Pu Zhou 《Advanced Photonics Nexus》 CSCD 2026年第2期207-221,共15页
Driven by artificial intelligence,the exponential growth in computational and energy demands has spurred exploration of alternative computing paradigms beyond traditional electronic processors.Optical neural networks(... Driven by artificial intelligence,the exponential growth in computational and energy demands has spurred exploration of alternative computing paradigms beyond traditional electronic processors.Optical neural networks(ONNs),a promising neuromorphic platform with light-speed computing,offer inherent advantages for accelerating artificial intelligence,including massive parallelism,ultralow latency,and reduced power consumption.However,most existing ONNs lack physical programmability after deployment due to rigid optical interconnects and limited optical transformations,which restrict their performance across diverse machine learning tasks.We propose a programmable and scalable ONN,an optoelectronic reservoir computing architecture integrated with cascaded linear disordered media.The key novelty lies in its physical kernel optimization via genetic algorithms,which involves programming diffuser orientations within a reduced search space and integrating wavefront shaping for feature preprocessing,thereby enhancing both optical random projection and preprocessing transformations.This platform achieves competitive accuracy in image classification with 93.5%fewer parameters than digital models;it also performs well in graph classification and human action recognition,proving feasibility for non-Euclidean and time-series datasets.Our work addresses the critical programmability bottleneck of ONNs,paving the way for scalable,energyefficient ONNs with programmable physical kernels and opening new paths for high-performance physicsinspired neuromorphic computing in resource-constrained scenarios. 展开更多
关键词 optical neural network deep learning optical linear transformation genetic algorithm disordered media
暂未订购 下载PDF
Neural Network Repair With Shapley-Guided Search 认领 引用
4
作者 Xiaofu Du Zixiong Zhang +2 位作者 Xuesong Wang Linqiang Liu Junyan Qian 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期816-834,共19页
The deployment of deep neural networks(DNNs)in safety-critical domains is critically hampered by their vulnerability to defects,which can arise from malicious attacks or low-quality data.Therefore,precisely locating t... The deployment of deep neural networks(DNNs)in safety-critical domains is critically hampered by their vulnerability to defects,which can arise from malicious attacks or low-quality data.Therefore,precisely locating the network components responsible for these defects,and subsequently repairing them without compromising overall model performance,presents a significant challenge.To address this,this paper introduces NSRepair,a framework that combines interpretable fault localisation with multi-objective optimisation.Specifically,to accurately attribute blame for a defect,we employ Shapley values to quantify the contribution of each neuron.To systematically manage the trade-off between defect correction and performance preservation,we formulate the repair task as a multi-objective optimisation problem.We conducted extensive experiments across four distinct repair tasks,validating NSRepair on diverse model architectures against seven specialised state-of-the-art methods.The results demonstrate that our unified framework effectively repairs a wide range of defects,demonstrating its potential as a versatile and practical solution for improving DNN dependability.Our code is publicly available at http://gffzzd3cc09b8251d45dfspfp0fcb669qw6kop.ffgz.tsg.suse.edu.cn/10.5281/zenodo.17494304. 展开更多
关键词 defect localization multi‐objective genetic algorithm neural network repair Shapley
暂未订购 下载PDF
Artificial Neural Network Surrogate Modelling for Predicting and Optimising CO2 Conversion to Methanol Under Uncertainty 认领 引用
5
作者 Muhammad Zulkefal Iftikhar Ahmad +2 位作者 Hakan Caliskan Hiki Hong Farooq Ahmad 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期633-645,共13页
This study introduces a data-driven surrogate modelling framework that combines an artificial neural network(ANN)with particle swarm optimisation(PSO)and a genetic algorithm(GA)to optimise methanol production under un... This study introduces a data-driven surrogate modelling framework that combines an artificial neural network(ANN)with particle swarm optimisation(PSO)and a genetic algorithm(GA)to optimise methanol production under uncertain conditions.A steady-state Aspen Plus model was developed and converted into dynamic mode by applying±5%uncertainty across 12 key process variables,generating 3880 data points that reflect realistic operational variability.The ANN model was trained and validated on the samples,achieving predictive accuracy(R2=0.988,RMSE=28.59)on unseen test data.Key features of the work include the use of the ANN as a surrogate model,its integration within PSO and GA optimisation frameworks and its application alongside Sobol and Fourier amplitude sensitivity test(FAST)methods to identify the most influential process variables affecting the methanol production rate.The proposed framework resulted in performance improvements,with PSO achieving an increase of 38.63%and GA 33.14%in methanol production.Cross-validation with the Aspen Plus model confirmed the reliability of the optimised operating conditions,with relative errors ranging from 0.07%to 2.15%.Overall,the study demonstrates the effectiveness of integrating surrogate modelling with intelligent optimisation techniques to improve the efficiency and robustness of methanol production processes under uncertainty. 展开更多
关键词 artificial neural networks(ANN) CO2hydrogenation genetic algorithm methanol production particle swarm optimisation surrogate modelling
暂未订购 下载PDF
Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network 认领 引用 被引量：23
6
作者 LONG Jiangqi LAN Fengchong +1 位作者 CHEN Jiqing YU Ping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2009年第1期36-41,共6页
For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet... For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet hardness,joint bottom diameter etc.,and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA)is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters.The training samples'parameters and the corresponding joints'mechanical properties are supplied to the artificial neural network(ANN)for training.The validating samples'experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints. 展开更多
关键词 genetic algorithm BP neural network mechanical clinching joint properties prediction
暂未订购 下载PDF
Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling 认领 引用 被引量：22
7
作者 徐元铭 李烁 荣晓敏 《Chinese Journal of Aeronautics》 EI CAS 2005年第4期310-316,共7页
Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to s... Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces. 展开更多
关键词 neural network genetic algorithm response surface composite structural optimization
暂未订购 下载PDF
Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm 认领 引用 被引量：25
8
作者 Dong-Jie Li Yang-Yang Li +1 位作者 Jun-Xiang Li Yu Fu 《International Journal of Automation and computing》 CSCD 2018年第3期267-276,共10页
Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the... Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA. 展开更多
关键词 Gesture recognition back propagation （BP） neural network chaos algorithm genetic algorithm data glove.
暂未订购 下载PDF
Optimizing the neural network hyperparameters utilizing genetic algorithm 认领 引用 被引量：23
9
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
暂未订购 下载PDF
Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm 认领 引用 被引量：9
10
作者 罗剑飞 林炜铁 +1 位作者 蔡小龙 李敬源 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第5期950-957,共8页
Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Exper... Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g 2 NO-2-N·(g MLSS)-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components (g·L-1 ): NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium. 展开更多
关键词 BP neural network genetic algorithm optimization nitrite oxidization rate nitrite-oxidizing bacteria
暂未订购 下载PDF
Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China 认领 引用 被引量：8
11
作者 Lin Chen Weibing Lin +3 位作者 Ping Chen Shu Jiang Lu Liu Haiyan Hu 《Journal of Earth Science》 SCIE CAS CSCD 2021年第4期828-838,共11页
A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an import... A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area. 展开更多
关键词 porosity prediction well logs back propagation neural network genetic algorithm Ordos Basin Yanchang Formation
暂未订购 下载PDF
Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm 认领 引用 被引量：6
12
作者 Morteza Vadood Majid Safar Johari Ali Reza Rahai 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第5期1937-1946,共10页
While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using po... While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96). 展开更多
关键词 hot mix asphalt fatigue property reinforced fiber artificial neural network genetic algorithm
暂未订购 下载PDF
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
13
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
暂未订购 下载PDF
The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm 认领 引用 被引量：6
14
作者 周凡贞 冯国泰 蒋洪德 《Chinese Journal of Aeronautics》 EI CAS 2003年第4期198-202,共5页
In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic alg... In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%. 展开更多
关键词 optimization design highly loaded rotating blades artificial neural network genetic algorithm
暂未订购 下载PDF
Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm 认领 引用 被引量：5
15
作者 沈虹 万健如 +2 位作者 张志超 刘英培 李光叶 《Transactions of Tianjin University》 EI CAS 2009年第4期245-248,共4页
Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic alg... Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance. 展开更多
关键词 elevator group control genetic algorithm neural network hybrid algorithm
暂未订购 下载PDF
Helicopter Sizing Based on Genetic Algorithm Optimized Neural Network 认领 引用 被引量：2
16
作者 卢新来 刘虎 +1 位作者 王钢林 武哲 《Chinese Journal of Aeronautics》 EI CAS 2006年第3期212-218,共7页
It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are s... It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are some difficulties in using NN, e.g., how to select a proper network structure and the number of hidden layers. In this paper, structure and connection weight of a three-layer NN are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. The proposed method can not only give an optimal NN structure and connection weight, but also reduce the prediction error and has the capability of self-learning when the latest data are available. Furthermore, this method can be easily applied to helicopter design systems. 展开更多
关键词 helicopter conceptual design sizing neural network genetic algorithm
暂未订购 下载PDF
Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm 认领 引用 被引量：3
17
作者 汪小凯 华林 +3 位作者 汪晓旋 梅雪松 朱乾浩 戴玉同 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第1期17-29,共13页
Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ri... Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process. 展开更多
关键词 vertical hot ring rolling dimension precision soft measurement model artificial neural network genetic algorithm
暂未订购 下载PDF
Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network 认领 引用 被引量：2
18
作者 LIU Gu WANG Liu-ying +1 位作者 CHEN Gui-ming HUA Shao-chun 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2011年第12期57-64,共8页
Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel.Experiments were carried out to characterize the hardening qualities.A predicting and optimizing model using gene... Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel.Experiments were carried out to characterize the hardening qualities.A predicting and optimizing model using genetic algorithm-back propagation neural network（GA-BP） was developed based on the experimental results.The non-linear relationship between properties of hardening layers and process parameters was established.The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results.The optimal properties of the hardened layer were deduced from GA.And through multi optimizations,the optimum comprehensive performances of the hardened layer were as follows：plasma arc current is 90 A,hardening speed is 2.2 m/min,plasma gas flow rate is 6.0 L/min and hardening distance is 4.3 mm.It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization. 展开更多
关键词 plasma transferred arc surface hardening optimization neural network genetic algorithm
暂未订购 下载PDF
Evolving Neural Networks Using an Improved Genetic Algorithm 认领 引用 被引量：6
19
作者 温秀兰 宋爱国 +1 位作者 段江海 王一清 《Journal of Southeast University(English Edition)》 CAS 2002年第4期367-369,共3页
A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal gen... A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove... 展开更多
关键词 genetic algorithms neural network nonlinear forecasting
暂未订购 下载PDF
Neural network and genetic algorithm based global path planning in a static environment 认领 引用 被引量：5
20
作者 杜歆 陈华华 顾伟康 《Journal of Zhejiang University-SCIENCE A》 CAS 2005年第6期549-554,共6页
Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network m... Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective. 展开更多
关键词 Mobile robot Neural network Genetic algorithm Global path planning Fitness function
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈