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Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks

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The Journal of China Universities of Posts and Telecommunications 2018年第1期25卷 15-28,36页

作者： Liu Ziluan Li Xin State Key Laboratory of networking and Switching Beijing University of Posts and Telecommunications

With the rapid growth of satellite traffic, the ability to forecast traffic loads becomes vital for improving data transmission efficiency and resource management in satellite networks. To precisely forecast the short-term traffic loads in satellite networks, a forecasting algorithm based on principal component analysis and a generalized regression neural network （PCA-GRNN） is proposed. The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic. Specifically, it selects optimal time series of spatio-temporally correlated historical traffic from satellites as forecasting inputs and applies principal component analysis to reduce the input dimensions while preserving the main features of the data. Then, a generalized regression neural network is utilized to perform the final short-term load forecasting based on the obtained principal components. The PCA-GRNN algorithm is evaluated based on real-world traffic traces, and the results show that the PCA-GRNN method achieves a higher forecasting accuracy, has a shorter training time and is more robust than other state-of-the-art algorithms, even for incomplete traffic datasets. Therefore, the PCA- GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.

关键词： satellite networks traffic load forecasting principal component analysis generalized regression neural network

Short-term Load Forecasting of Regional Distribution network Based on generalized regression neural network Optimized by Grey Wolf Optimization Algorithm

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CSEE Journal of Power and Energy Systems 2021年第5期7卷 1093-1101页

作者： Leijiao Ge Yiming Xian Zhongguan Wang Bo Gao Fujian Chi Kuo Sun Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300132China China Electric Power Research Institute Co.Ltd Beijing 100192China. State Grid Tianjin Electric Power Company Tianjin 300010China.

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong *** paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input *** that the high-precision short-term丨oad forecasting based on GWO-GRNN model is *** is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast *** a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

关键词： Factor analysis generalized regression neural network gray wolf optimization maximum information coefficient short-term load forecasting

Risk based security assessment of power system using generalized regression neural network with feature extraction

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Journal of Central South University 2013年第2期20卷 466-479页

作者： M. Marsadek A. Mohamed College of Engineering Universiti Tenaga Nasional Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

关键词： generalized regression neural network line overload low voltage principle component analysis risk index voltagecollapse

Application of generalized regression neural network on fast 3D reconstruction

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Journal of Harbin Institute of Technology(New Series) 2007年第1期14卷 9-12页

作者： Babakhani Asad 杜志江孙立宁 Kardan Reza Mianji A. Fereidoun Robotics Institute Harbin Institute of Technology Radiation Protection Department National Regulatory Authority Organization of Iran

In robot-assisted surgery projects,researchers should be able to make fast 3D reconstruction. Usually 2D images acquired with common diagnostic equipments such as UT, CT and MRI are not enough and complete for an accurate 3D reconstruction. There are some interpolation methods for approximating non value voxels which consume large execution time. A novel algorithm is introduced based on generalized regression neural network (GRNN) which can interpolate unknown voxles fast and reliable. The GRNN interpolation is used to produce new 2D images between each two succeeding ultrasonic images. It is shown that the composition of GRNN with image distance transformation can produce higher quality 3D shapes. The results of this method are compared with other interpolation methods practically. It shows this method can decrease overall time consumption on online 3D reconstruction.

关键词： generalized regression neural network 3 D reconstruction visualization

generalized regression neural network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy

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Generalized Regression Neural Network Based Quantitative Str...

Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network

2012 National Conference on Information Technology and Computer Science

作者： Hui Li School of Computer Science and Information Technology Northeast Normal University Changchun,Jilin,China Yinghua Lu School of Computer Science and Information Technology Northeast Normal University Changchun,Jilin,China Ting Gao School of Computer Science and Information Technology Northeast Normal University Changchun,Jilin,China Hongzhi Li School of Computer Science and Information Technology Northeast Normal University Changchun,Jilin,China Lihong Hu School of Computer Science and Information Technology Northeast Normal University Changchun,Jilin,China

generalized regression neural network （GRNN） was used to develop a quantitative structure-property relationship （QSPR） model to improve the calculation accuracy of density functional theory （DFT）.The model has been applied to evaluate optical absorption energies of 150 organic molecules based on the molecular *** entire dataset was divided into a training set of 120 molecules and a test set of 30 molecules according to the method,termed SPXY （Sample set Partitioning based on joint x-y distances）,extended Kennard and Stones （KS） algorithm according to their differences in both x （instrumental responses） and y （predicted parameter） spaces in the calculation of inter-sample ***-propagation neural network with SPXY partitioning algorithm （BPNN-SPXY） and GRNN with KS algorithm （GRNN-KS） were also utilized to construct model to compare with the results obtained by GRNN with SPXY algorithm （GRNN-SPXY）.The root-mean-square errors in absorption energy predictions for the whole data set given by DFT,BPNN-SPXY,GRNN-KS and GRNN-SPXY were 0.47,0.21,0.17 and 0.13,*** GRNN-SPXY prediction results are in good agreement with the experimental value of absorption energy.

关键词： generalized regression neural network Sample subset partitioning Kennard and Stones algorithm Absorption energy Density functional theory

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Journal of Chongqing University 2006年第3期5卷 131-136页

作者：梁宗保 The Key Laboratory for Optoelectronic Technology & system Under the State Ministry of Education Chongqing UniversityChongqing 400030 P.R. China

As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.

关键词： parameter optimization exponentially weighted moving average control chart generalized regression neural network

Recognition of License Plate Character Based on Wavelet Transform and generalized regression neural network

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Recognition of License Plate Character Based on Wavelet Tran...

Wheat Grain Protein Content Estimation Based on Multi-temporal Remote Sensing Data and generalized regression neural network

第24届中国控制与决策会议

作者： Wang Yutao, Qin Tingting, Tian Ruixia, Yang Gang Northeastern University, Shenyang 110004

Recognition methods of Chinese characters and similar characters are the main factors which affect the rate of license plate character recognition system. In this paper, a hybrid method based on wavelet transform and generalized regression neural network （GRNN） is proposed. For Chinese characters, a block projection with wavelet packet transform is adopted to extract the feature and a clustering algorithm is presented to reduce the dimension of feature vector. Moreover, the method of the characteristic regional recognition is introduced to recognition the similar characters. Besides, the clustering method based on wavelet transform is used to extract the feature of letter and digital characters. Furthermore, GRNN with powerful non-linear mapping capability and high fault-tolerant ability is designed as character classifier in the multi-network character recognition system. Finally, the result shows that the recognition rate of whole network could reach up to 91% and the classification algorithm has better robustness.

关键词： License plate character recognition Wavelet transform Wavelet packet transform generalized regression neural network

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Wheat Grain Protein Content Estimation Based on Multi-tempor...

An Approach to Carbon Emissions Prediction Using generalized regression neural network Improved by Genetic Algorithm

Computer and Computing Technologies in Agriculture V——5th IFIP TC 5/SIG 5.1 Conference(CCTA2011) Part II

作者： Cunjun Li Qian Wang Jihua Wang Yan Wang Xiaodong Yang Xiaoyu Song Wenjiang Huang Beijing Research Center for Information Technology in Agriculture

Monitoring grain protein content in large areas by remote sensing is very important for guiding graded harvest,and facilitates grain purchasing for processing *** grain protein content(GPC)at maturity was measured and multi-temporal Landsat TM and Landsat ETM+images at key stages in 2003,2004 growth stages were acquired in this *** was estimated with multi-temporal remote sensing data and generalized regression neural network(GRNN)*** show that the GPC prediction accuracy of the GRNN model is higher,with the average relative deviation of self-modeling,average relative deviation of cross-validation as 0.003%,0.321%;4.300%,7.349%for 2003 and 2004 *** method proves to be reliable and robust to monitoring GPC in large areas by multitemporal and multi-spectral remote sensing data.

关键词： wheat grain protein content multi-temporal Landsat generalized regression neural network

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Electrical Science & Engineering 2020年第1期2卷 4-10页

作者： Zhida Guo Jingyuan Fu School of Economics and Management Dalian Jiaotong UniversityDalianChina Academic Unit of Human Communication Developmentand Information SciencesFaculty of educationthe University of Hong KongHong KongChina

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change *** the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of *** algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in *** the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into *** target values were obtained after the calculation of the predicted ***,compared with the results of GRNN,the improved method realized higher prediction *** thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

关键词： Carbon emissions Genetic Algorithm generalized regression neural network Smooth Factor Prediction