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Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 182-186页

作者： Kim, Byungwhan Kwon, Sanghee Kim, Dong Hwan Sejong Univ Dept Elect Engn Seoul 143747 South Korea Seoul Natl Univ Technol Sch Mech Design & Automat Engn Seoul 139743 South Korea

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. in this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens I and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box-Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96 x 10(-12) for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Scanning electron microscope Resolution generalized regression neural network Genetic algorithm model Statistical experiment Lens Statistical regression model

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Modeling thermal conductivity of hydrogen-based binary gaseous mixtures using generalized regression neural network

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 59卷 242-250页

作者： Naghizadeh, Arefeh Amiri-Ramsheh, Behnam Atashrouz, Saeid Abuswer, Meftah Ali Abedi, Ali Mohaddespour, Ahmad Hemmati-Sarapardeh, Abdolhossein Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Amirkabir Univ Technol Dept Chem Engn Tehran Polytech Tehran Iran Amer Univ Middle East Coll Engn & Technol Kuwait Kuwait McGill Univ Dept Chem Engn Montreal PQ H3A 0C5 Canada China Univ Petr State Key Lab Petr Resources & Prospecting Beijing Peoples R China

The accurate measurement of the thermal conductivity of hydrogen is crucial in efficiently integrating hydrogen into diverse applications that encompass storage, utilization, and production. This paper introduces strong intelligent algorithms capable of predicting the thermal conductivity of hydrogen-based binary gaseous mixtures consisting of carbon monoxide (CO), methane (CH4), and carbon dioxide (CO2), based on the mole fractions of these components, pressure, and temperature. To this end, four advanced intelligent models, namely Radial Basis Function (RBF), Cascade Forward neural network (CFNN), Multilayer Perceptron (MLP), and generalized regression neural network (GRNN) were developed. After that, an analysis of graphical and statistical errors was conducted to further examine the precision of the proposed models. The findings revealed a strong alignment between the suggested models and experimental values. Additionally, among the developed models, the GRNN technique was identified as the superior forecasting approach with the (RMSE = 0.00129) and the highest value of the coefficient of determination, (R2 = 0.9987). Moreover, the sensitivity analysis of the GRNN model indicates that hydrogen composition has the highest influence on the thermal conductivity of hydrogen-based gaseous mixtures, as evidenced by the relevancy factor value of 0.881. The assessment of group errors also demonstrates that the proposed models exhibit high accuracy when confronted with higher temperatures and pressures. Ultimately, the Leverage analysis was employed to evaluate the dependability of the new paradigm, revealing that more than 97% of the data falls within the applicable range of the paradigm.

关键词： Thermal conductivity Binary gaseous mixtures Hydrogen Machine learning generalized regression neural network

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A generalized regression neural network Model to Predict CFA Piles Performance Using Borehole and Static Load Test Data

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第4期48卷 4403-4419页

作者： Ibrahim, Farid Alzo'ubi, Abdelkareem Odhabi, Hamad Abu Dhabi Univ Coll Engn Al Ain U Arab Emirates Abu Dhabi Univ Al Ain U Arab Emirates

This research aims to predict the downward displacement of piles as the axial loading increases and decreases to form the pile static load curves. The generalized regression neural network (GRNN) is used to predict the behavior of continuous flight auger piles through training and testing the network by using field data collected from over 100 field static load tests. The model aims to reduce cost, decrease the dependency on the lengthy field test, and complement the current methods used in pile design. In this research, the authors built a neural network that associates the ground properties, such as friction angle and unconfined compressive strength, as well as the pile's length and diameter with the CFA pile performance at different sites. More than six thousand field points were collected to train the neural network. Moreover, three independent field tests were used to examine the performance of the network comparing the predicted values with the field data. The statistical measures showed that the proposed model can be generalized. The model can predict the pile displacement curves accurately as long as the testing data fall within the training set. If the testing data fall outside the range, the model was not as accurate. To evaluate the performance of the GRNN, three statistical measures were calculated;the coefficient of efficiency ranged between 0.8362 and 0.9968, the root mean square error ranged between 0.0868 and 0.2760, and the mean absolute error ranged between 0.070469 and 0.23443.

关键词： Pile static load test generalized regression neural network Statistical measures United Arab Emirates

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Confined Aquifer's Hydraulic Parameters Estimation by a generalized regression neural network

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2020年第1期44卷 259-269页

作者： Delnaz, Atefeh Rakhshandehroo, Gholamreza Nikoo, Mohammad Reza Shiraz Univ Dept Civil & Environm Engn Shiraz Iran

Precise estimation of aquifer hydraulic parameters can be an asset to more sustainable management of groundwater resources. The nonlinear correlation of aquifer parameters along with equation-level complicacy of aquifers' charging functions can be efficiently handled using artificial intelligence models due to their flexibility in mapping between the observed data and output functions. This study focuses on performance comparisons for generalized regression neural network, artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) for estimation of hydraulic parameters, namely storage coefficient and transmissibility of confined aquifers. To acquire a greater precision of the estimated output functions, the principle component analysis of the pumping test data was initially undertaken to reduce the input dimensions by filtering out redundancies and insignificant variables' correlations. Next, these data are passed into the training and validation process of the artificial intelligence models. Several error indices, mean absolute relative error (MARE), RMSE, MAE, RMRE, Bias and R-2 were used during the validation of the prediction models. Finally, the estimated output functions were compared against the traditional and yet commonly used graphical Theis method. As an example, MARE in aquifer parameters estimation by ANN and graphical Theis method was 0.5564% and 1.1320%, respectively. More generally, among all the intelligence prediction models used to estimate the hydraulic parameters of a confined aquifer, ANFIS was more accurate and sensibly required much less computational time than the others and, hence, may be selected as the superior model in aquifer parameters estimation.

关键词： Groundwater management Aquifer parameter estimation Artificial intelligence models Artificial neural network generalized regression neural network

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Chaos-generalized regression neural network prediction model of mine water inflow

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SN APPLIED SCIENCES 2021年第12期3卷 1-11页

作者： Li, Jianlin Wang, Luyang Wang, Xinyi Gao, Peiqiang Henan Polytech Univ Inst Resources & Environm Jiaozuo 454000 Henan Peoples R China Collaborat Innovat Ctr Coal Work Safety & Clean H Jiaozuo 454000 Henan Peoples R China

Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.

关键词： Chaos theory Phase space reconstruction generalized regression neural network Input layer neuron Long-term prediction of mine water

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An epileptic seizure detection system based on cepstral analysis and generalized regression neural network

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2018年第2期38卷 201-216页

作者： Yavuz, Erdem Kasapbasi, Mustafa Cem Eyupoglu, Can Yazici, Rifat Istanbul Commerce Univ Dept Comp Engn Istanbul Turkey

This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Epileptic seizure detection Cepstral analysis Electroencephalogram generalized regression neural network

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An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro-in vivo correlation

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EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES 2007年第3-4期30卷 264-272页

作者： Parojcic, Jelena Ibric, Svetlana Djuric, Zorica Jovanovic, Milica Corrigan, Owen I. Univ Belgrade Dept Pharmaceut Technol Fac Pharm Belgrade 11221 Serbia Univ Dublin Trinity Coll Sch Pharm & Pharmaceut Sci Dublin 2 Ireland

Quantitative correlations between in vivo and in vitro data (IVIVC) reduce the number of human in vivo studies, thus decreasing the overall time and expenses necessary for the development of optimal drug product formulation. Although linear regression analysis represents the simplest relationship, it is recognized that IVIVC should not be limited to linear relationship. With regards to the implementation of non-linear IVIVC models and the ability of artificial neural network (ANN) computing to cope with non-linear relationships, the usefulness of ANN analysis in the development of IVIVC merits further evaluation. The present paper is an attempt to develop an IVIVC for model sustained release paracetamol matrix tablet formulations employing various correlation approaches based on linear and non-linear modeling of in vitro and in vivo data. Currently accepted compendial methodology was compared with the alternative approaches, involving general mixed effects model and generalized regression neural network (GRNN) analysis, in order to evaluate their usefulness for predicting the in vivo behavior of drug products. Although based on analogous approaches, data generated by GRNN were closer to those observed in vivo, leading to the higher level of IVIVC than obtained by convolution. It can be assumed that GRNN analysis was able to generalize complex relations between the output and input parameters and could account for the differences in drug release kinetics observed under various conditions in vitro, thus offering potential as a reliable and robust estimate of drug products in vivo behavior. (c) 2006 Elsevier B.V. All rights reserved.

关键词： in vitro-in vivo correlations generalized regression neural network dissolution paracetamol Carbopol (R)

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Precipitable water vapor fusion based on a generalized regression neural network

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JOURNAL OF GEODESY 2021年第3期95卷 1-14页

作者： Zhang, Bao Yao, Yibin Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

Water vapor plays an important role in Earth's weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used.

关键词： Precipitable water vapor Data fusion Artificial neural network generalized regression neural network

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Application of generalized regression neural network on the prediction of the main girths of young females based on 3D point cloud data

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2017年第6-7期20卷 1355-1358页

作者： Kuang, Cai-Yuan Jinling Inst Technol Coll Art Nanjing 211169 Jiangsu Peoples R China

The widths and thicknesses of 110 female undergraduates are obtained from the extracted silhouette, the girths are measured by using software Imageware based on 3D point cloud data, which corresponding to input and output units of the optimized generalized regression neural network (GRNN) and independent and dependent variables of the multiple linear regression (MLR) model. The training and test samples are used to establish and test the validity of GRNN and MLR model. The result shows that the GRNN is superior to the MLR.

关键词： generalized regression neural network Multiple linear regression Point cloud data Girth Prediction

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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

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