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Use of Wavelet Transform and generalized regression neural network (GRNN) to the Characterization of Short-Duration Voltage Variation in Electric Power System

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IEEE LATIN AMERICA TRANSACTIONS 2009年第2期7卷 217-222页

作者： Machado, R. N. D. M. Bezerra, U. H. Pelaes, E. G. Oliveira, R. C. L. D. Tostes, M. E. D. L. Fed Univ Para Dept Engn Eletr & Computacao BR-66059 Belem Para Brazil

This work presents the use of the wavelet transform and computational intelligence techniques to quantify voltage short-duration variation in electric power systems, with respect to time duration and magnitude. The wavelet transform is used to determine the event duration, as well as for obtaining a characteristic curve relating the signal norm as function of the number of cycles for a waveform without disturbance that is used as reference for the calculation of the magnitude of the event. A generalized regression neural network (GRNN) is used to interpolate not stored points of the characteristic curve. The method is part of a process to automate the post operation signal analysis in electric power systems, and it is used to quantify the voltage short-duration variation magnitude of previously selected signals. The method has been shown efficient, and some results obtained from the application of this method to power system real signals are presented.

关键词： quality generalized regression neural network wavelet transform

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generalized regression neural network For Long-Term Electricity Load Forecasting

Generalized Regression Neural Network For Long-Term Electric...

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Smart Technology and Applications (ICoSTA), 2020 International Conference on

作者： Widi Aribowo Supari Muslim Ismet Basuki Department Of Electrical Engineering State University Of Surabaya Surabaya Indonesia

ISBN: (数字)9781728130835

ISBN: (纸本)9781728130842

The availability of electricity demand is very high. Many households and industrial equipment are using electricity as the source energy. The reliability of the power system in saving the budget is very much needed. This can be succeeded by doing good and proper operation planning. The important step of the electric power system operation planning is to predict load electricity. The load forecasting can support the corporations of electricity to assign the cost and power generation. Long-term forecasting is a technique of predicting periods for more than one year. The old data will be a guide to solve the issues. In this research, the concept of generalized regression neural network (GRNN) is to predict long-term electricity load. The advantage of the GRNN method can estimate the absolute function between input and output data sets directly from training data. The research was compared to the results of the actual data, Feed Forward Backpropagation neural network (FFBNN), Cascade Forward Backpropagation neural network (CFBNN) and generalized regression neural network (GRNN). The results of the study will be measured and validated using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) methods.

关键词： neural network Cascade Forward Backpropagation generalized regression neural network Load Forecasting General regression neural networks load forecasting neural network Electricity power systems

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An interpolation method based on generalized regression neural network for ultrasonic 3D reconstruction.

An interpolation method based on generalized regression neur...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Asad, Babakhani Du Zhi-Jiang Sun Li-ning Fereidoon, Mianji Abdollah Reza, Kardan Mohammad Harbin Inst Technol Mechatron Dept Harbin 150006 Peoples R China Natl Regulatory Author Org Radiat Protect Dept Tehran Iran

ISBN: (纸本)9781424402588

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 We introduce a novel algorithm 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 and conserve the quality on online 3D reconstruction.

关键词： generalized regression neural network 3D reconstruction visualization

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

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

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5th International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Li, Cunjun Wang, Qian Wang, Jihua Wang, Yan Yang, Xiaodong Song, Xiaoyu Huang, Wenjiang Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China

ISBN: (纸本)9783642272776

Monitoring grain protein content in large areas by remote sensing is very important for guiding graded harvest, and facilitates grain purchasing for processing enterprises. Wheat 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 study. GPC was estimated with multi-temporal remote sensing data and generalized regression neural network (GRNN) method. Results 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 respectively. GRNN method proves to be reliable and robust to monitoring GPC in large areas by multi-temporal and multi-spectral remote sensing data.

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

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Public Bicycle Prediction Based on generalized regression neural network 2nd

Public Bicycle Prediction Based on Generalized Regression Ne...

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2nd International Conference on Internet of Vehicles (IOV)

作者： Yang, Min Guang, Yingnan Zhang, Xuedan Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9783319272931;9783319272924

Nowadays building a green and efficient public transportation system for the expanding urban population is undoubtedly a big challenge. In recent years, public bicycle system has been widely appreciated and researched worldwide. Unlike traditional public transportation system, public bicycle system doesn't need to follow fixed schedule. This flexibility brings high efficiency as well as uncertainty-we don't know whether there are available bikes or bike stands when they are indeed needed. This paper aims to predict the number of available bikes at given future time point so as to optimize the user's travel choices. In this article, we propose a new prediction model and use generalized regression neural network as our prediction algorithm to optimize prediction accuracy. Experimental results show that in this way we can properly handle this nonlinear problem.

关键词： Public bicycle system Prediction model generalized regression neural network

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

Generalized Regression Neural Network Based Quantitative Str...

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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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Battery State of Health Estimation Using The generalized regression neural network 8

Battery State of Health Estimation Using The Generalized Reg...

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Proceedings 2015 8 International Congress on Image and Signal Processing (CISP)

作者： Zhou, Jie He, Zhiwei Gao, Mingyu Liu, Yuanyuan Hangzhou Dianzi Univ Dept Elect & Informat Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781467390989

Batteries have been widely used in the field of electric vehicles. So prediction of the state of health (SOH) is important to the safe and efficient use of them. In this study, SOH is estimated by the generalized regression neural network (GRNN). GRNN is established by the radial basis neurons and linear neurons. The network has the advantages of approximation ability and the learning speed. In this test, there are 12 pieces of Li-ion batteries. Constant current charging and discharging are performed on them, until the capacity drops to below 80% of nominal. The SOH of the battery is estimated by the data that obtained from the operation. The data from the test shows that the recharging time by the constant current on the battery, the instantaneous voltage drops in discharge, and the output energy under a certain depth of discharge (DOD) are important to estimate the SOH of battery. The data from the 6 pieces of batteries are performed to train the GRNN. And the feasibility of this method is verified by the data from the other batteries. The test shows the difference of the SOH of the battery can be estimated accurately by this method, and it has great significance in the performance improvement of the battery management system.

关键词： lithium-ion battery state of health generalized regression neural network

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A Study of Cutting Tool Wear Prediction Utilizing generalized regression neural network with Improved Fruit Fly Optimization

A Study of Cutting Tool Wear Prediction Utilizing Generalize...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Kang, Ling Xiong, Xin Yi, Lili Guo, Yijun Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China

ISBN: (纸本)9781538653807

Tool wear prediction becomes increasingly important due to the growing demand for finished quality and the improvement of productivity. In this case, it is necessary to establish a well-designed monitoring system to obtain the relationship between tool wear and cutting process. generalized regression neural network (GRNN) is able to handle non-linear problems with its memory-based character. However, it was rarely used for tool wear prediction in the past several decades. Therefore, in this paper, it was employed to tackle this problem. In addition, in order to tune the smooth parameter of the GRNN, a newly proposed evolutionary algorithm called fruit fly optimization algorithm (FOA) was adopted. Meanwhile, an improved fruit fly optimization algorithm (IFOA), in which escaping and distance control parameters were introduced to prevent FOA from falling into local optimum, was presented to enhance the search ability. Two cutting experiments showed that the IFOA-GRNN provided a comparable regression ability to the GRNN with particle swarm optimization(PSO), the least squares support vector machines(LS-SVM) and the BP neural network.

关键词： Tool wear generalized regression neural network improved fruit fly optimization algorithm smooth parameter escaping and distance control parameters

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Supervised Pterygium Fibrovascular Redness Grading Using generalized regression neural network 13

Supervised Pterygium Fibrovascular Redness Grading Using Gen...

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13th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT)

作者： Che Azemin, Mohd Zulfaezal Hilmi, Mohd Radzi Mohd Kamal, Khairidzan Int Islamic Univ Malaysia Kulliyyah Allied Hlth Sci Dept Optometry & Visual Sci Kuala Lumpur Malaysia Int Islamic Univ Malaysia Dept Ophthalmol Kulliyyah Med Kuala Lumpur 53100 Malaysia

ISBN: (纸本)9781614994343;9781614994336

Pterygium is a growth on the eye that can cause blindness, with countries closer to the equator showing higher rate of incidence. However, there is a lack of research to study the severity and properties of the tissue. We propose the use of generalized neural network (GRNN) to objectively quantify redness of the fibrovascular tissue. Comparative analysis using multiple feature selection algorithms indicates that error can be minimized when use with optimal set of features and suitable GRNN spread parameter. Features nominated by Minimum Redundancy Maximum Relevance gives the best performance with SSE = 3.55 and GRNN spread = 0.47.

关键词： Image color analysis generalized regression neural network supervised learning

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Using a generalized regression neural network to Improve the Accuracy of Determination of the Rail Track Trajectory

Using a Generalized Regression Neural Network to Improve the...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Nguyen Trong Yen Boronakhin, Alexander M. Larionov, Daniil Yu Nguyen Quoc Khanh St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781665404761

A method for increasing the accuracy of determination of the object's movement trajectory (using a railcar as an example) is considered. The method is based on fusing the data acquired from the satellite navigation system receiver during multiple passages along the same section of the rail track The data approximation is carried out using a generalized regression neural network The paper considers the problem of determining a functional relationship connecting the required number of passages, the desired positioning accuracy, and the initial error of the satellite navigation system receiver. The result of this work serves as basis for building an electronic map of rail tracks.

关键词： generalized regression neural network satellite navigation system rail track trajectory electronic map approximation

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