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15,297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：
Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index 认领 引用 被引量：18
1
作者 Xiuliang Jin Zhenhai Li +2 位作者 Haikuan Feng Zhibin Ren Shaokun Li 《The Crop Journal》 SCIE CSCD 2020年第1期87-97,共11页
Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the bes... Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region. 展开更多
关键词 Biomass estimation Maize Vegetation indices Deep neural network algorithm LAI
暂未订购 下载PDF
A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation 认领 引用 被引量：5
2
作者 Qifeng Jiang Mianshui Rong +1 位作者 Wei Wei Tingting Chen 《Journal of Earth Science》 SCIE CAS CSCD 2024年第4期1355-1366,共12页
Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method bas... Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training. 展开更多
关键词 seismic topographic effect finite element method BP neural network algorithm earthquake disaster prevention
暂未订购 下载PDF
A Self-organization Mapping Neural Network Algorithm and Its Application to Identify Ecosystem Service Zones 认领 引用 被引量：18
3
作者 战金艳 史娜娜 +1 位作者 吴红 邓祥征 《Agricultural Science & Technology》 CAS 2009年第5期162-165,共4页
The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem A... The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers. 展开更多
关键词 Neural network algorithm Ecosystem services Ecosystem service zones Sustainable ecosystem management
暂未订购 下载PDF
Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data 认领 引用 被引量：2
4
作者 Leyang Wang Ranran Xu Fengbin Yu 《Geodesy and Geodynamics》 EI CSCD 2022年第4期386-398,共13页
The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-line... The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment. 展开更多
关键词 Fault parameter inversion Genetic algorithm Nelder-Mead simplex algorithm Neural network algorithm
暂未订购 下载PDF
Adaptive control of parallel manipulators via fuzzy-neural network algorithm 认领 引用 被引量：3
5
作者 Dachang ZHU Yuefa FANG 《控制理论与应用（英文版）》 2007年第3期295-300,共6页
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric u... This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF. 展开更多
关键词 Parallel manipulator Adaptive control Fuzzy neural network algorithm Simulation
暂未订购 下载PDF
Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm 认领 引用
6
作者 Musaed Alrashidi 《Computers, Materials & Continua》 SCIE EI 2023年第4期1073-1088,共16页
Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regi... Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54. 展开更多
关键词 Weibull probability density function wind energy numerical estimation method metaheuristic optimization algorithm neural network algorithm
暂未订购 下载PDF
Agricultural Water Resources Utilization and Management under Agricultural Safety Aim Based on Fuzzy Neural Network Algorithm 认领 引用
7
作者 Shengxin WAN 《Asian Agricultural Research》 2021年第12期1-4,8,共4页
[Objectives]To explore the agricultural water resources utilization and management under the agricultural safety aim.[Methods]Fuzzy neural network algorithm was adopted.The evaluation model of agricultural water resou... [Objectives]To explore the agricultural water resources utilization and management under the agricultural safety aim.[Methods]Fuzzy neural network algorithm was adopted.The evaluation model of agricultural water resources utilization and management carrying capacity based on quantitative system was established.[Results]With the remarkable improvement of China's national income,great progress has been made in China's agricultural development.However,in the process of agricultural safety production,the problem of sustainable development has not been noticed,the problem of water resources exceeding the limit bearing capacity frequently occurs.[Conclusions]It is of great significance to effectively solve the problem of water resources utilization and management.In the feasibility test for the algorithm,further tests on various indicators show that the research is feasible. 展开更多
关键词 Fuzzy neural network algorithm Quantification system Utilization and management of water resources
暂未订购 下载PDF
Multiple learning neural network algorithm for parameter estimation of proton exchange membrane fuel cell models 认领 引用 被引量：3
8
作者 Yiying Zhang Chao Huang +1 位作者 Hailong Huang Jingda Wu 《Green Energy and Intelligent Transportation》 2023年第1期1-15,共15页
Extracting the unknown parameters of proton exchange membrane fuel cell(PEMFC)models accurately is vital to design,control,and simulate the actual PEMFC.In order to extract the unknown parameters of PEMFC models preci... Extracting the unknown parameters of proton exchange membrane fuel cell(PEMFC)models accurately is vital to design,control,and simulate the actual PEMFC.In order to extract the unknown parameters of PEMFC models precisely,this work presents an improved version of neural network algorithm(NNA),namely the multiple learning neural network algorithm(MLNNA).In MLNNA,six learning strategies are designed based on the created local elite archive and global elite archive to balance exploration and exploitation of MLNNA.To evaluate the performance of MLNNA,MLNNA is first employed to solve the well-known CEC 2015 test suite.Experimental results demonstrate that MLNNA outperforms NNA on most test functions.Then,MLNNA is used to extract the parameters of two PEMFC models including the BCS 500 W PEMFC model and the NedStack SP6 PEMFC model.Experimental results support the superiority of MLNNA in the parameter estimation of PEMFC models by comparing it with 10 powerful optimization algorithms. 展开更多
关键词 Neural network algorithm Parameter extraction Proton exchange membrane fuel cell Metaheuristics
Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling 认领 引用 被引量：22
9
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：26
10
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
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
11
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
Helicopter Sizing Based on Genetic Algorithm Optimized Neural Network 认领 引用 被引量：2
12
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
Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network 认领 引用 被引量：2
13
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
Fuzzy optimization neural network model based on LM algorithm 认领 引用 被引量：1
14
作者 彭勇 周惠成 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第3期431-436,共6页
A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In ... A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability. 展开更多
关键词 fuzzy optimization neural network Levenberg-Marquardt algorithm transfer function
暂未订购 下载PDF
Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network 认领 引用 被引量：7
15
作者 QIN Qiang FENG Yunwen LI Feng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第6期1317-1326,共10页
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co... The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm. 展开更多
关键词 structural reliability enhanced cuckoo search(ECS) artificial neural network(ANN) cuckoo search(CS) algorithm
暂未订购 下载PDF
Model algorithm control using neural networks for input delayed nonlinear control system 认领 引用 被引量：2
16
作者 Yuanliang Zhang Kil To Chong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第1期142-150,共9页
The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. ... The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems. 展开更多
关键词 model algorithm control neural network nonlinear system time delay
暂未订购 下载PDF
Multiple model tracking algorithms based on neural network and multiple process noise soft switching 认领 引用 被引量：2
17
作者 NieXiaohua 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第6期1227-1232,共6页
A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are runn... A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation. 展开更多
关键词 maneuvering target current statistical model neural network multiple model algorithm.
暂未订购 下载PDF
An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification 认领 引用 被引量：7
18
作者 Bao-Chang Xu Ying-Ying Zhang 《International Journal of Automation and computing》 CSCD 2014年第4期434-440,共7页
Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to impr... Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation system.Compared with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance. 展开更多
关键词 Gravitational search algorithm orbital change optimization neural network system identification
暂未订购 下载PDF
A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal 认领 引用 被引量：4
19
作者 Deling Zheng, Ruixin Liang, Ying Zhou, and Ying WangInformation Engineering School, University of Science and Technology Beijing, Beijing 100083, China 《Journal of University of Science and Technology Beijing》 2003年第2期68-71,共4页
A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the... A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased. 展开更多
关键词 blast furnace optimization chaos genetic algorithm neural network silicon content prediction
暂未订购 下载PDF
A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks 认领 引用
20
作者 张长江 付梦印 金梅 《Journal of Beijing Institute of Technology》 EI CAS 2003年第2期119-124,共6页
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very relucta... A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis. 展开更多
关键词 multi layer feed forward neural networks BP algorithm Newton recursive algorithm
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