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Underwater Positioning Based on an Artificial Lateral Line and a generalized regression neural network

Journal of Bionic Engineering 2018年第5期15卷 883-893页

作者： Xiande Zheng Yong Zhang Mingjiang Ji Ying Liu Xin Lin Jing Qiu Guanjun Liu Science and Technology on Integrated Logistics Support Laboratory National University of Defense Technology Changsha 410073 China College of Intelligence Science National University of Defense Technology Changsha 410073 China

Taking advantage of the lateral line organ, fish can navigate, feed, and avoid predators and obstacles by sensing surrounding flow fields. The lateral line organ provides an important reference for the development of new underwater detection technology. Inspired by the lateral line organ, in this paper, for the sake of localizing the target dipole source in three-dimensional underwater space, an artificial lateral line consisting of nine underwater pressure sensors forming a cross-shaped sensor array is applied. Combined with the method of gener- alized regression neural network, which is suitable for solving nonlinear pattern recognition problems, a corresponding experimental platform has been built to sample data for training the neural network from a 12 cm by 12 cm by 24 cm cuboid space. The experimental results indicate that the cross-shaped artificial lateral line can localize the target dipole source two body-lengths away. The well- performing perceptual distance is below 13 cm away from the sensing array. Moreover, decreasing the data sampling interval and in- creasing the number of sensors utilized can help improve the positioning accuracy.

关键词： lateral line underwater positioning generalized regression neural network bionics

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Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 136卷 71-78页

作者： Feng, Yu Peng, Yong Cui, Ningbo Gong, Daozhi Zhang, Kuandi Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr State Engn Lab Efficient Water Use Crops & Disast MOA Key Lab Dryland Agr Beijing Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling Peoples R China

Accurate estimation of reference evapotranspiration (ET0) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET0 as benchmark. Two data management scenarios were evaluated for estimation of ET0: (1) the models were trained/calibrated and tested using the local data of each station;and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267 mmid and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263 mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Reference evapotranspiration Temperature data Extreme learning machine generalized regression neural network Hargreaves model

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A generalized regression neural network-based method for predicting cumulative infiltration volume in shallow slopes

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2021年第9期80卷 7269-7285页

作者： Li, S. H. Liu, G. G. Zhang, S. X. Chengdu Univ Technol State Key Lab Geohazard Prevent Protect Chengdu 610059 Sichuan Peoples R China

Developing a method for quickly estimating the cumulative infiltration in shallow slopes is essential for engineers to evaluate slope stability. In this paper, the hydraulic parameters of the Yan'an loess in China were obtained through physical mechanics test. Numerical experiments with different slope inclinations, soil thicknesses, rainfall intensities, and initial moisture contents were carried out to obtain the cumulative infiltration curves. The Philip model was used to fit the cumulative infiltration curves. The slope inclination, soil thickness, rainfall intensity, and initial moisture content were served as the input variables, and the parameters of the Philip model were the output variables. A generalized regression neural network-based method was proposed to estimate the cumulative infiltration quickly. Three indicators (coefficient of determination, mean squared error, and mean absolute error) show that the performance of the proposed method is superior to those of the multiple linear regression model, the back propagation neural network, and radial basis function neural network. Parametric analysis indicates that rainfall intensity is the most sensitive parameter affecting cumulative infiltration.

关键词： Shallow slope Cumulative infiltration Loess generalized regression neural network Philip model

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Road accident risk prediction using generalized regression neural network optimized with self-organizing map

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neural COMPUTING & APPLICATIONS 2022年第11期34卷 8511-8524页

作者： Kaffash Charandabi, Neda Gholami, Amir Abdollahzadeh Bina, Ali Univ Tabriz Marand Tech Fac Dept Geomat Tabriz Iran

A road accident risk map that is designed to locate high-risk areas is an efficient way to reduce road traffic injuries and fatalities. To produce an accurate road accident risk map, it is required to compute the probability of the accident's occurrence by considering the various variables that contribute to road accidents. To this end, this research proposed a generalized regression neural network tuned with self-organizing map to estimate the risk of road accidents. This hybrid predictive model estimates the road accident risk by considering 22 different predictor variables (features), including geographic characteristics, temporal conditions, weather conditions, road-related characteristics, vehicle-related characteristics, and driver characteristics calculated based on the authoritative data sources and volunteered geographic information (VGI). The required VGI was collected in this study by developing a third-party application that was run inside telegram messenger. To evaluate the performance and usability of the proposed model for estimation of the accident risk along the road, the developed model was applied to the Tabriz-Marand dual carriageway, Iran. In this sense, 30 different scenarios were designed, and for each scenario, the risk of the accident was predicted at 3008 points along the Tabriz-Marand dual carriageway. A quality assessment of the proposed approach for different scenarios demonstrated that the predicted accident risk had very high accuracy (average accuracy about 90.74%). According to the results of this research, distance from traffic control cameras, day of the week, driver's age, weather, elevation, and vehicle type were the most effective factors in high-risk areas of the study area.

关键词： Accident data Risk map generalized regression neural network Volunteered geographic information Environmental factors Individual factors

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Development and comparison of backpropagation and generalized regression neural network models to predict diurnal and seasonal gas and PM₁₀ concentrations and emissions from swine buildings

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TRANSACTIONS OF THE ASABE 2008年第2期51卷 685-694页

作者： Sun, G. Hoff, S. J. Zelle, B. C. Nelson, M. A. Iowa State Univ Dept Agr & Biosyst Engn Ames IA 50011 USA

The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R-2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling.

关键词： backpropogation diurnal gas generalized regression neural network PM10 seasonal swine buildings

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A combined generalized regression neural network wavelet model for monthly streamflow prediction

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KSCE JOURNAL OF CIVIL ENGINEERING 2011年第8期15卷 1469-1479页

作者： Kisi, Ozgur Erciyes Univ Dept Civil Engn Fac Engn TR-38039 Kayseri Turkey

The ability of a combined model, Wavelet-generalized regression neural network (WGRNN), is investigated in the current study for the prediction of monthly streamflows. The WGRNN model is obtained by combining two methods, Discrete Wavelet Transform (DWT) and generalized regression neural network (GRNN), for one-month-ahead streamflow forecasting. The monthly flow data of two stations, the Gerdelli Station on the Canakdere River and the Isakoy Station on the Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. The forecasts of the WGRNN model are tested using the Root Mean Square Error (RMSE), Variance Account For (VAF) and correlation coefficient (R) statistics and the results are compared with those of the single GRNN and Feed Forward neural network (FFNN). The comparison results revealed that the WGRNN performs better than the GRNN and FFNN models in monthly streamflow prediction. For the Gerdelli and Isakoy stations, it is found that the WGRNN models with RMSE = 5.31 m(3)/s, VAF = 52.3%, R = 0.728 and RMSE = 3.36 m(3)/s, VAF = 55.1%, R = 0.742 in the test period are superior in forecasting monthly streamflows than the best accurate GRNN models with RMSE = 6.39 m(3)/s, VAF = 30.1%, R = 0.553 and RMSE = 4.19 m(3)/s, VAF = 30.1%, R = 0.549, respectively.

关键词： streamflow generalized regression neural network discrete wavelet transform prediction

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generalized regression neural network Based Channel Identification and Compensation Using Scattered Pilot

Generalized Regression Neural Network Based Channel Identifi...

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作者： He, H. Brno University of Technology

In the high-speed mobile environment, channel state information (CSI) estimated at the beginning of the packet is quite different at the last part because the actual channel state changes with time. To overcome this problem, a neural network (NN) based channel compensation method was previously developed. Due to inaccurate channel estimation of decision feedback channel estimation (DFCE), the pilot-aided CSI of the first symbol and DFCE-aided CSIs in the intermediate data part will cause inexact channel state transition even though the application of NN. Accordingly, the channel compensation performance is still degraded, especially in the last part of the packet. This paper proposes a new version of GRNN based channel identification and compensation method by introducing scattered pilot. It can improve the tracking capability of GRNN thanks to densely arranged pilot in the time-domain while it cannot reduce the transmission efficiency. Simulation results show that the proposed method is more effective than the conventional ones in terms of RMSE and BER performance, even in the fast fading environment.

关键词： OFDM fast fading channel estimation generalized regression neural network DFCE scattered pilot

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A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1999年第1期48卷 47-58页

作者： Chtioui, Y Panigrahi, S Francl, L N Dakota State Univ Dept Plant Pathol Fargo ND 58105 USA N Dakota State Univ Agr & Biosyst Engn Dept Fargo ND 58105 USA

The objective of this paper was the development of an optimal generalized regression neural network (GRNN) for leaf wetness prediction. The GRNN prediction results were compared to those obtained with the standard multiple linear regression (MLR), Leaf wetness, which is difficult to measure directly, has an important effect on the development of disease on plants. in this study, leaf wetness was predicted from micrometeorological factors (temperature, relative humidity, wind speed, solar radiation and precipitation). Simulations showed than the MLR provided an average absolute prediction error of 0.1300 for the training set and 0.1414 for the test set. The GRNN provided an average absolute prediction errors of 0.0491 and 0.0894 on the same data sets, respectively. This error is very low since the leaf wetness initially varies between 0 and 1. The optimized GRNN, therefore, outperformed the MLR in terms of the prediction accuracy. However, the GRNN required more computational time than the MLR. The main disadvantage of the MLR is that it assumes a linear relationship between the feature to be predicted and the measured features. The GRNN automatically extracts the appropriate regression model (linear or nonlinear) from the data. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： generalized regression neural network multiple linear regression prediction leaf wetness plant disease

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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

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Mineral deposit grade assessment using a hybrid model of kriging and generalized regression neural network

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neural COMPUTING & APPLICATIONS 2022年第13期34卷 10611-10627页

作者： Singh, Rahul K. Ray, D. Sarkar, B. C. Indian Sch Mines Indian Inst Technol Dept Appl Geol Dhanbad 826004 Bihar India Indian Sch Mines Automat Ctr Indian Inst Technol Dhanbad 826004 Bihar India

Artificial neural networks are powerful global approximators for mineral grade assessment. The techniques are capable of retaining nonlinearity and spatial heterogeneity of a feature variable and can even perform modelling with noisy and incomplete data. These qualifiers make neural networks strong contenders for mineral grade assessment. In the present research, the authors have proposed a hybrid model consisting of two unsupervised models, namely ordinary kriging (OK) and k-means clustering, used as pre-processing steps to feed data to supervised generalized regression neural network (GRNN) model. While OK models the spatial variability and provides estimation on local scale, k-means clustering has been used for dimensionality reduction. These steps support in achieving accuracy and speed of the modelling process. GRNN model prepares its training and validation datasets using the k-clusters. Testing and validation of the model have been carried out on five live iron-ore deposits. Once the validation is found adequate and acceptable generalization with validation dataset is achieved, the model is verified with testing dataset. Deposit-wise value of R-2 of the hybrid GRNN model has been found to vary between 0.93 and 0.99. The model is observed to deliver improved performance when compared with multi-layer perceptron, radial basis function and recurrent neural network models. Spatially distributed estimation maps generated retain nonlinearity and spatial heterogeneity of the original Fe data values. Thus, the hybrid GRNN model provides an edge over the standalone classical geostatistics or standalone GRNN model.

关键词： Mineral grade assessment Ordinary kriging generalized regression neural network Hybrid model

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