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Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and generalized regression neural network

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REMOTE SENSING 2021年第14期13卷 2718-2718页

作者： Xu, Xitong Chen, Shengbo Ren, Liguo Han, Cheng Lv, Donglin Zhang, Yufeng Ai, Fukai Jilin Univ Coll GeoExplorat Sci & Technol Changchun 130026 Peoples R China 10th Geol Brigade Co Ltd Liaoning Prov Fushun 113004 Peoples R China

With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils.

关键词： visible and near-infrared spectroscopy heavy metals fractional-order derivative optimal band combination algorithm generalized regression neural network

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A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm

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Neurocomputing 2017年 221卷 24-31页

作者： Rui Hu Shiping Wen Zhigang Zeng Tingwen Huang School of Automation Huazhong University of Science and Technology Wuhan PR China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan PR China Texas A & M University at Qatar Doha 5825 Qatar

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ . Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error.

关键词： Short term power load forecasting generalized regression neural network Fruit fly optimization algorithm Decreasing step size

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

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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

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A hybrid model of generalized regression neural network and radial basis function neural network for wind power forecasting in Indian wind farms

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2020年第1期23卷 49-63页

作者： Varanasi, Jyothi Tripathi, M. M. Delhi Technol Univ Dept Elect Engn New Delhi 110042 India

Wind power generation is a major part of renewable energy but it suffers from uncertainty and variable nature of wind speed. In conquering the uncertainty, forecasting of wind power generation emerges as best solution. Accordingly, wind power forecasting assists the power grid to increase the grid reliability. In this paper, a generalized regression neural network (GRNN), a neural network of radial basis function (RBFN) and a hybrid of GRNN and RBFN are applied for estimation of wind power and their performance is compared in respect to short-term wind power forecasting. In comparison to RBFN, GRNN has simple and static architecture. Historical wind power and meteorological wind speed data of the year 2014 from Indian wind farms are employed for training and testing the neural networks. The research work carried in this article interprets that GRNN has consistently performed better than RBFN. The hybrid GRNN-RBFN has also depicted good accuracy in a week ahead wind power forecasting. In such cases, either GRNN or RBFN predicts with high errors the hybrid GRNN-RBFN is helpful in accurate wind power forecasting. The errors are calculated in terms of MAPE & RMSE. For GRNN approach MAPE ranges from 0.48 % to 10.53 % and RMSE ranges from 0.01 KW to 3.31 KW. Whereas the maximum MAPE is 25.6 % for RBFN and the maximum RMSE is much higher i.e 11.61KW. In fact, for any forecasting approach, reliability is a major concern. The work carried also includes reliability analysis of all three forecasting models proposed. To ensure the best performance of GRNN, confidence intervals of MAPE are computed for all three models implemented. Among all, GRNN is well perceived to be the best one with narrowest confidence intervals.

关键词： Wind power forecasting radial basis function neural network generalized regression neural network hybrid GRNN-RBFN optimization data mean absolute percentage error

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Tool wear monitoring using generalized regression neural network

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ADVANCES IN MECHANICAL ENGINEERING 2019年第5期11卷 105页

作者： Kang, Ling Wang, Sibao Wang, Shilong Ma, Chi Yi, Lili Zou, Haixu Chongqing Univ Coll Mech Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learning speed and fast convergence to the optimal results whether the sample data are small or large. The low predictive accuracy and efficiency are caused by traditionally manual adjustment of the spread parameters in generalized regression neural network and then the improved fruit fly optimization algorithm is proposed to optimize the spread parameters of regression neural network automatically. Combining the improved fruit fly optimization and generalized regression neural network, the tool wear prediction method is proposed in the paper. Various experiments are carried out to validate the proposed method and the comparison results show a good agreement. In addition, the proposed method is compared to the tool wear prediction method in the literature, and the comparison results also show that the proposed method can achieve better performance.

关键词： Tool wear monitoring generalized regression neural network improved fruit fly optimization algorithm particle swarm optimization

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BDS multipath real-time estimation based on generalized regression neural network

BDS multipath real-time estimation based on generalized regr...

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第八届中国卫星导航学术年会

作者： Zongpeng Pan Hongzhou Chai Yang Chong Yulong Kong Rui Wang Institute of Surveying and Mapping Information Engineering University

ISBN: (纸本)9789811045875

Multipath is the main factor that affects the quality of the pseudo-range observation and the single point positioning. It is difficult to model and weaken the effect of multipath,due toits nonlinear and *** contribution presents a multipath real-time estimation method based on generalized regression neural network(GRNN). The real-time multipath is estimated using training samples based on sliding window data. Due to the nonlinear approximation property of GRNN, the BDS multipath is effectively reduced. Finally, the validity of real time multipath estimation based on GRNN is verified using MGEX data.

关键词： Multipath BDS generalized regression neural network GEO/IGSO/MEO

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

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

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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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The Research of Tax Inspection Based-on generalized regression neural network

The Research of Tax Inspection Based-on Generalized Regressi...

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2012 International Conference on Intelligent Control and Information Processing (ICIP 2012)

作者： Shihong Chen Xiaoqing Liu Guangdong University of Foreign Studies Jinan University Guangdong University of Finance

This paper tries to use the generalized regression neural network(in short: GRNN) to assist tax inspection case selection. First, this paper briefly introduces the theory of generalized regression neural network and applies it in the tax inspection. Second, it analyzes the financial statements and tax returns of 93 commercial enterprises, and then establishes the GRNN model and gets the analyzing result. Finally, the result is compared with the known taxation case. Then we get the conclusion that the generalized regression neural network method can help the tax inspection case selection and improve the efficiency and effectiveness of inspection work.

关键词： Tax Inspection generalized regression neural network

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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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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

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 *** 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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Path Attenuation of Prediction Wireless Sensor network Signal in Corn Field Based on the generalized regression neural network

Path Attenuation of Prediction Wireless Sensor Network Signa...

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2013 2nd International Conference on Mechanical Engineering,Industrial Electronics and Informatization(MEIEI 2013)

作者： He Pan Taihao Li zhigang Liu Information technology teaching and Management Center Jilin Agricultural University Key Laboratory of Mine Informatization Henan Polytechnic University

In order to solve the problem whether the wireless sensor network(WSN) nodes are quickly and reasonably arranged in the corn field,this paper proposes the prediction of wireless signal path loss in the corn field on the basis of generalized regression neural *** this test,this paper takes carrier frequency of 433 MHz and *** to the features of radio transmission,the corn is divided into three different growth period to measure the path *** value is the output Expectation *** influenced factors,namely the growth period,the transmitter antenna height,receiver antenna height,antenna gain,the carrier frequency and communication distance,are the input *** to this,the GRNN prediction model is established.

关键词： Wireless sensor network(WSN) generalized regression neural network Attenuation radio frequency signal Corn field

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