检索结果-内蒙古大学图书馆

Finite element and generalized regression neural network modelling of multiple cracks growth under the influence of multiple crack parameters

引用

MULTIDISCIPLINE MODELING IN MATERIALS AND STRUCTURES 2023年第5期19卷 1014-1041页

作者： Hidayat, Mas Irfan P. Pramata, Azzah D. Airlangga, Prima P. Inst Teknol Sepuluh Nopember Dept Mat & Met Engn Surabaya Indonesia

PurposeThis study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack ***/methodology/approachTo determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were *** FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of *** limitations/implicationsThe models are presented for static multiple crack growth problems. No material anisotropy is *** implicationsIn practical crack-growth analyses, the NN approach provides significant benefits and ***/valueThe proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

关键词： Crack growth modeling Multiple cracks Finite element generalized regression neural network Complex input-output relationship Efficient nonlinear regression model

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization of fiber laser cutting based on generalized regression neural network and non-dominated sorting genetic algorithm

引用

INFRARED PHYSICS & TECHNOLOGY 2020年 108卷 103337-103337页

作者： Ding, Hua Wang, Zongcheng Guo, Yicheng Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China

An integrated model based on generalized regression neural network (GRNN) and non-dominated sorting genetic algorithm (NSGAII) with elite strategy is proposed to predict and optimize the quality characteristics of fiber laser cutting stainless steel. An orthogonal experiment has been conducted where laser power, cutting speed, gas pressure, defocus are considered as controllable input parameters with kerf width and surface roughness as output to generate the dataset for the model. In GRNN-NSGAII model, the cross-validation method was performed to train the network to obtain the optimal GRNN. Significance of controllable parameters of laser on outputs is also discussed. The GRNN model is determined as the fitness function for prediction and calculation during the NSGAII optimization process. NSGAII generates complete optimal solution set with Pareto optimal front for outputs. The prediction relative error of GRNN model is within +/- 5%. Experimental verification error of optimized output less than 5%. Characterization of the process parameters in Pareto optimal region has been described in detail.

关键词： Fiber laser cutting generalized regression neural network Non-dominated sorting genetic algorithm Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform

引用

neural COMPUTING & APPLICATIONS 2019年第4期31卷 1127-1142页

作者： Chin, Cheng Siong Ji, Xi Woo, Wai Lok Kwee, Tiaw Joo Yang, Wenxian Newcastle Univ Fac Sci Agr & Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Ngee Ann Polytech Marine & Offshore Technol Ctr I 535 Clementi Rd Singapore 599489 Singapore

A modified multiple generalized regression neural network (GRNN) is proposed to predict the noise level of various compartments onboard of the offshore platform. With limited samples available during the initial design stage, GRNN can cause errors when it maps the available inputs to sound pressure level for the entire offshore platform. To obtain more relevant group for GRNNs training, fuzzy C-mean (FCM) is used. However, outliers in some group may interfere the prediction accuracy. The problem of selecting suitable inputs parameters (in each cluster) is often impeded by lack of accurate information. Principal component analysis (PCA) is used to ensure high relevance input variables in each cluster. By fusing multiple GRNNs by an optimal spread parameter, the proposed modeling scheme becomes quite effective for modeling multiple frequency-dependent data set (ranging from 125 to 8000Hz) with different input parameters. The performance of FCM-PCA-GRNNs has improved significantly as the results show a 25% improvement on the spatial sound pressure level (SPL) and 85% improvement on the spatial average SPL than just GRNNs alone. By comparing with data obtained from real engine room on a jack-up rig, the FCM-PCA-GRNNs noise model performs better with around 16% less error than the empirical-based acoustic models. Additionally, the results show comparable performance to statistical energy analysis that requires more time and resources to solve during the early stage of the offshore platform design.

关键词： Fuzzy C-mean Principal component analysis generalized regression neural network Noise prediction Offshore platform

来源：评论

学校读者我要写书评

暂无评论

Predicting lightning outages of transmission lines using generalized regression neural network

引用

APPLIED SOFT COMPUTING 2019年 78卷 438-446页

作者： Xie, Yunyun Li, Chaojie Lv, Youjie Yu, Chen Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Technol Univ Sch Engn Royal Melbourne Inst Melbourne Vic Australia State Grid Wuxi Power Supply Co Wuxi 214062 Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing 211106 Jiangsu Peoples R China

Lightning is the major cause of transmission line outages, which can result in large area blackouts of power systems. One effective method to prevent catastrophic consequences is to predict lightning outages before they occur. The abundance of recorded lightning and lightning outage data in power system makes it possible to predict lightning outages of transmission lines. This paper proposes an artificially intelligent algorithm using general regression neural networks (GRNN) to predict lightning outages of transmission lines. First, the data that can be obtained from the operation and management system of a power company are analyzed, and the features that can be used as input parameters of GRNN are extracted. The prediction model based on GRNN is then built to perform lightning outage prediction. Finally, the effectiveness of the proposed method is validated by comparing it with (Back Propagation) BP and (Radial Basis Function) RBF neural networks using actual lightning data and lightning outage data. The simulation results show that the proposed method provides much better prediction performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Transmission line Lightning outage Outage prediction generalized regression neural network

来源：评论

学校读者我要写书评

暂无评论

Learning experiments with genetic optimization of a generalized regression neural network

引用

DECISION SUPPORT SYSTEMS 1996年第3-4期18卷 317-325页

作者： Hansen, JV Meservy, RD Marriott School of Management Brigham Young University 540 N. Eldon Tanner Building P.O. Box 23068 Provo UT 84602-3068 USA

This paper reports a study unifying optimization by genetic algorithm with a generalized regression neural network, Experiments compare hill-climbing optimization with that of a genetic algorithm, both in conjunction with a generalized regression neural network, Controlled data with nine independent variables are used in combination with conjunctive and compensatory decision forms, having zero percent and 10 percent noise levels. Results consistently favor the GRNN unified with the genetic algorithm.

关键词： genetic algorithm generalized regression neural network radial basis function

来源：评论

学校读者我要写书评

暂无评论

Prediction of plasma etching using a randomized generalized regression neural network

引用

VACUUM 2004年第1期76卷 37-43页

作者： Kim, B Lee, DW Park, KY Choi, SR Choi, S Korea Univ Dept Elect & Informat Engn Chochiwon 339700 Choongnam South Korea Sejong Univ Dept Elect Engn Bio Engn Res Ctr Seoul 143747 South Korea

A new empirical technique to construct predictive models of plasma etch processes is presented. This was accomplished by combining a generalized regression neural network (GRNN) and a random generator (RG). The RG played a critical role to control neuron spreads in the pattern layer. The proposed R-GRNN was evaluated with experimental plasma etch data. The etching of silica thin films was characterized by a 2(3) full factorial experiment. The etch responses examined include aluminium etch rate, silica etch rate, profile angle, and DC bias. Additional test data were prepared to evaluate model appropriateness. Compared to conventional GRNN, the R-GRNN demonstrated much improved predictions of more than 40% for all etch responses. This was illustrated over statistical regression models. As a result, the proposed R-GRNN is an effective way to considerably improve the predictive ability of conventional GRNN. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： plasma etching random generator generalized regression neural network statistical regression model

来源：评论

学校读者我要写书评

暂无评论

A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

引用

JOURNAL OF WATER AND CLIMATE CHANGE 2022年第2期13卷 854-871页

作者： Dutta, Diljit Bhattacharjya, Rajib Kumar Indian Inst Technol Guwahati Dept Civil Engn Gauhati Assam India

Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network - a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951-2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006-2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

关键词： bias correction general circulation models generalized regression neural network near-surface temperature

来源：评论

学校读者我要写书评

暂无评论

A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

引用

KNOWLEDGE-BASED SYSTEMS 2013年第Jan.期37卷 378-387页

作者： Li, Hong-ze Guo, Sen Li, Chun-jie Sun, Jing-qi N China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Annual power load forecasting generalized regression neural network Fruit fly optimization algorithm Optimization problem Parameter selection

来源：评论

学校读者我要写书评

暂无评论

Wind Power Forecasting Based on Ensemble Empirical Mode Decomposition with generalized regression neural network Based on Cross-Validated Method

引用

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2019年第5期14卷 1823-1829页

作者： Cai, Huanhuan Wu, Zhihui Huang, Chao Huang, Daizheng Guangxi Vocat Coll Technol & Busyness Nanning 530003 Peoples R China Guangxi Med Univ Dept Biomed Engn Nanning 530021 Peoples R China

The growth of wind power connected to the power grid has increased the importance of accurate wind power prediction that exhibits non-linearity and non-stationarity. The goal of this study is to forecast wind power by using the generalized regression neural network (GRNN) coupled with ensemble empirical mode decomposition (EEMD) and assessment of prediction accuracy. EEMD technologies are used to perform decomposition, and each intrinsic mode function is predicted and forecasted by using a GRNN based on cross-validated parameters. The forecasting results of the sub-series are superimposed as the results of wind power prediction. Results show that the proposed method has high prediction accuracy and is highly effective in forecasting wind power.

关键词： Wind power forecasting Ensemble empirical mode decomposition generalized regression neural network Cross-validated method

来源：评论

学校读者我要写书评

暂无评论

Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

引用

HYDROLOGY RESEARCH 2017年第4期48卷 1156-1168页

作者： Feng, Yu Gong, Daozhi Mei, Xurong Cui, Ningbo Chinese Acad Agr Sci State Engn Lab Efficient Water Use Crops & Disast MOA Key Lab Dryland Agr Inst Environm & Sustainable Dev Agr Beijing Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu Sichuan Peoples R China

Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (h(c)) were continuously measured during maize growing seasons of 2011-2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (K-c) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

关键词： evapotranspiration extreme learning machine generalized regression neural network maize modified dual crop coefficient approach

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：