Статті в журналах: "GRNNs"

1

Catasus, Miguel, Wayne Branagh, and Eric D. Salin. "Improved Calibration for Inductively Coupled Plasma-Atomic Emission Spectrometry Using Generalized Regression Neural Networks." Applied Spectroscopy 49, no. 6 (June 1995): 798–807. http://dx.doi.org/10.1366/0003702953964444.

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Artificial neural networks have been recently used in different fields of science in applications ranging from pattern recognition to semi-quantitative analysis. In this work, two types of neural networks were applied to the problems of spectral interferences, matrix effects, and the measurement drift in ICP-AES. Their performance was compared to that of the more conventional technique of multiple linear regressions (MLR). The two types of neural networks examined were “traditional” multilayer perceptron neural networks and generalized regression neural networks (GRNNs). The GRNN is comparable to, or better than, MLR for modeling spectral interferences and matrix effects covering several orders of magnitude. In the case of an Fe spectral interference on Zn, the GRNN reduced the error from 81% to 24%, while MLR reduced the average error to only 49%. For matrix effects caused by large backgrounds of Mg (0–10,000 ppm) on Zn, average error was reduced to 55% from 67%. In the case of combinations of spectral overlaps and matrix effects, the GRNN reduced average error by approximately 10%. MLR performed poorly on systems involving matrix effects. GRNN is also a very promising tool for the correction of drift caused by fluctuations in power levels, reducing drift over a two-hour period from 2.3% to 0.6%. GRNNs, both by themselves and in multinetwork combinations, seem to be highly promising for the correction of nonlinear matrix effects and long-term signal drift in ICP-AES.

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2

Dang, Hieu V., and Witold Kinsner. "Optimal Colour Image Watermarking Using Neural Networks and Multiobjective Memetic Optimization." International Journal of Neural Networks and Advanced Applications 9 (March 11, 2022): 23–32. http://dx.doi.org/10.46300/91016.2022.9.5.

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This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of the GRNN are searched by a MOMA for optimally embedding watermark bits into wavelet coefficients. The experimental results show that the proposed approach achieves robustness and imperceptibility in watermarking.

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3

Günaydın, Kemal, and Ayten Günaydın. "Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey." Mathematical Problems in Engineering 2008 (2008): 1–20. http://dx.doi.org/10.1155/2008/919420.

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Three different artificial neural network (ANN) methods, namely, feed-forward back-propagation (FFBP), radial basis function (RBF), and generalized regression neural networks (GRNNs) were applied to predict peak ground acceleration (PGA). Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances.

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4

Zhang Geng Xu Hao Wen Wu. "The Study on Vocal Print Recognition basing on the GRNNs." International Journal of Digital Content Technology and its Applications 6, no. 14 (August 31, 2012): 291–97. http://dx.doi.org/10.4156/jdcta.vol6.issue14.36.

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5

Feng, Yu, Daozhi Gong, Xurong Mei, and Ningbo Cui. "Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau." Hydrology Research 48, no. 4 (August 30, 2016): 1156–68. http://dx.doi.org/10.2166/nh.2016.099.

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Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (Kc) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

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6

Hussain, Hafezali Iqbal, Nazratul Aina Mohamad Anwar, and Mohd Shahril Ahmad Razimi. "A generalised regression neural network model of financing imbalance: Shari’ah compliance as the roadmap for sustainability of capital markets." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5387–95. http://dx.doi.org/10.3233/jifs-189023.

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The current study looks at the impact of compliance to Shari’ah principles on the capital structure for Malaysian firms. Examination of impact of compliance is based on the classification by the Securities Commission of Malaysia. Given that the literature on adjustment tends to ignore non-linear models, the current study utilises Generalised Regression Neural Network (GRNNs). Results are compared to conventional panel data regression models via performing a hold-out sample. Initial results confirm stability of the data allowing predictive ability. The results indicate that compliant firms tend to finance a greater portion of their financing imbalance via equities relative to non-compliant firms. This provides a strong indication towards compliant firms reducing overall risk taking where the financing pattern incorporates a greater aspect of risk sharing which is in-line with Shari’ah principles. In addition, two more factors are ranked as important in deciding compliant firms issue choice to resolve financial imbalance: profitability and size. The rest of the determinants have low impact on explaining net debt issues. Diagnostics for results provide evidence of lower RMSE and MSE for GRNNs for the training, testing and overall datasets. The potential benefit of this research allows managers and investors of Islamic capital markets to understand potential risk exposure and financing costs of compliant firms. Findings also provide a roadmap for development of a sustainable capital market model which has wider implications on a global scale.

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7

STUBBERUD, PETER. "A VECTOR MATRIX REAL TIME RECURSIVE BACKPROPAGATION ALGORITHM FOR RECURRENT NEURAL NETWORKS THAT APPROXIMATE MULTI-VALUED PERIODIC FUNCTIONS." International Journal of Computational Intelligence and Applications 08, no. 04 (December 2009): 395–411. http://dx.doi.org/10.1142/s1469026809002667.

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Unlike feedforward neural networks (FFNN) which can act as universal function approximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTRBP) algorithm in a vector matrix form is developed for a two-layer globally recursive neural network that has multiple delays in its feedback path. This algorithm has been evaluated on two GRNNs that approximate both an analytic and nonanalytic periodic multi-valued function that a feedforward neural network is not capable of approximating.

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8

Joshua, Vinson, Selwin Mich Priyadharson, and Raju Kannadasan. "Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu." Agronomy 11, no. 10 (October 15, 2021): 2068. http://dx.doi.org/10.3390/agronomy11102068.

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

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9

Tkachenko, Roman, Ivan Izonin, Natalia Kryvinska, Ivanna Dronyuk, and Khrystyna Zub. "An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble." Sensors 20, no. 9 (May 4, 2020): 2625. http://dx.doi.org/10.3390/s20092625.

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The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

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10

Li, Juan, Zhiqiang Xiao, Rui Sun, and Jinling Song. "Retrieval of the Leaf Area Index from Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance Based on Unsupervised Domain Adaptation." Remote Sensing 14, no. 8 (April 10, 2022): 1826. http://dx.doi.org/10.3390/rs14081826.

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Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map the MODIS and VIIRS surface reflectance into the same subspace to reduce the distribution discrepancies between the MODIS and VIIRS surface reflectance. Then, the embedded data obtained from MODIS surface reflectance dataset, along with the LAI values produced by fusing the MODIS and the Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products, were employed to train general regression neural networks (GRNNs). Finally, for retrieving the LAI values, the embedded data acquired from the VIIRS surface reflectance dataset was input into the trained GRNNs. For multiple field sites with different biome types, we used this developed method to retrieve LAI values based on the VIIRS surface reflectance dataset. The results indicate that, based on the training dataset built from MODIS surface reflectance dataset, the domain adaptation-based retrieval method can effectively estimate LAI values from VIIRS surface reflectance dataset. By comparison with the VIIRS and MODIS LAI products, the retrieved LAI values with TCA are more consistent with the reference LAI values acquired from high-resolution remote sensing images. The coefficient of determination (R2) and root mean square error (RMSE) of the retrieved LAI values with TCA at all selected sites are 0.88 and 0.68, respectively. Furthermore, the accuracy of the retrieved LAI values with TCA is higher than the retrieved LAI values without TCA with the R2 0.81 and the RMSE 0.79.

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11

Song, Dengwei, Hongmei Liu, Le Qi, and Bo Zhou. "A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers." Complexity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4763612.

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A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network (PNN) acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments.

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12

Aengchuan, Prasert, Attasit Wiangkham, Niti Klinkaew, Kampanart Theinnoi, and Ekarong Sukjit. "Prediction of the influence of castor oil–ethanol–diesel blends on single-cylinder diesel engine characteristics using generalized regression neural networks (GRNNs)." Energy Reports 8 (November 2022): 38–47. http://dx.doi.org/10.1016/j.egyr.2022.10.113.

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13

Liu, Duanyang, Linqing Yang, Kun Jia, Shunlin Liang, Zhiqiang Xiao, Xiangqin Wei, Yunjun Yao, Mu Xia, and Yuwei Li. "Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods." Remote Sensing 10, no. 10 (October 16, 2018): 1648. http://dx.doi.org/10.3390/rs10101648.

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Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

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14

Kartal, Serkan, Mustafa Oral, and Buse Melis Ozyildirim. "Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map." International Journal of Applied Mathematics and Computer Science 28, no. 2 (June 1, 2018): 411–24. http://dx.doi.org/10.2478/amcs-2018-0031.

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Abstract In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.

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15

Vilavicencio-Arcadia, Edgar, Silvana G. Navarro, Luis J. Corral, Cynthia A. Martínez, Alberto Nigoche, Simon N. Kemp, and Gerardo Ramos-Larios. "Application of Artificial Neural Networks for the Automatic Spectral Classification." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/1751932.

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Classification in astrophysics is a fundamental process, especially when it is necessary to understand several aspects of the evolution and distribution of the objects. Over an astronomical image, we need to discern between stars and galaxies and to determine the morphological type for each galaxy. The spectral classification of stars provides important information about stellar physical parameters like temperature and allows us to determine their distance; with this information, it is possible to evaluate other parameters like their physical size and the real 3D distribution of each type of objects. In this work, we present the application of two Artificial Intelligence (AI) techniques for the automatic spectral classification of stellar spectra obtained from the first data release of LAMOST and also to the more recent release (DR5). Two types of Artificial Neural Networks were selected: a feedforward neural network trained according to the Levenberg–Marquardt Optimization Algorithm (LMA) and a Generalized Regression Neural Network (GRNN). During the study, we used four datasets: the first was obtained from the LAMOST first data release and consisted of 50731 spectra with signal-to-noise ratio above 20, the second dataset was obtained from the Indo-US spectral database (1273 spectra), the third one (the STELIB spectral database) was used as an independent test dataset, and the fourth dataset was obtained from LAMOST DR5 and consisted of 17990 stellar spectra with signal-to-noise ratio above 20 also. The results in the first part of the work, when the autoconsistency of the DR1 data was probed, showed some problems in the spectral classification available in LAMOST DR1. In order to accomplish a better classification, we made a two-step process: first the LAMOST and STELIB datasets were classified by the two IA techniques trained with the entire Indo-US dataset. The resulted classification allows us to discriminate at least three groups: the first group contained O and B type stars, whereas the second contained A, F, and G type stars, and finally, the third group contained K and M type stars. The second step consisted of a refinement of the classification, but this time for every group, the most relevant indices were selected. We compared the accuracy reached by the two techniques when they are trained and tested using LAMOST spectra and their published classification and the resultant classifications obtained with the ANNs trained with the Indo-US dataset and applied over the STELIB and LAMOST spectra. Finally, in the first part, we compared the LAMOST DR1 classification with the classification obtained by the application of the NNs GRNNs and LMA trained with the Indo-US dataset. In the second part of the paper, we analyze a set of 17990 stellar spectra from LAMOST DR5 and the very significant improvement in the spectral classification available in DR5 database was verified. For this, we trained ANNs using the k-fold cross-validation technique with k = 5.

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16

Singh, Siddhartha Kumar, Harlal Singh Mali, Deepak Rajendra Unune, Szymon Wojciechowski, and Dominik Wilczyński. "Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study." Processes 10, no. 4 (April 13, 2022): 755. http://dx.doi.org/10.3390/pr10040755.

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Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) were developed and compared to assess their performance with limited training data. Various smoothing factors and kernels were tested for GRNN and GPR, respectively. The prediction of models was compared in terms of the mean absolute percentage error, root mean square error, and coefficient of determination. The results showed that GPR outperforms GRNN and accurately predicts the μ-EDM process responses. The GRNN’s performance was better for less stochastic output with a discernible pattern than other outputs. The Automatic Relevance Determination (ARD) squared exponential kernel was found to be the best performing kernel among those chosen. GPR models can be used with reasonable accuracy to predetermine critical process outputs as they have R2 values above 0.90 for both training and validation data for all outputs. This work paves the way for future industrial implementation of GPR to model and predict the outputs of complex hybrid machining processes.

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17

Peeters, Piet-Hein. "Grens." Zorg + Welzijn 23, no. 12 (December 2017): 7. http://dx.doi.org/10.1007/s41185-017-0181-5.

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Jansson, Oscar. "Maskinkroppens gräns." Tidskrift för litteraturvetenskap 51, no. 1-2 (December 10, 2021): 153–71. http://dx.doi.org/10.54797/tfl.v51i1-2.1747.

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Boundaries of the Machine Body: Violence, Immunity and Media Assemblages in The Last of Us This article examines the portrayal of bodily boundaries in the videogame series The Last of Us. Drawing on theories of media ecology and posthumanism (most notably Deer’s notion of radical animism, Haraway’s theories of the cyborg, and Fuller’s account of media assemblages), three aspects of this portrayal are described: first, the game’s narrativization of bodily violence through an amalgamation of the player’s sensory systems with media technologies; second, the game’s depiction of monstrous corporeality; and third, its representation of immune systems through the mirrored relationship between external tools and endogenous bodily functions. Connecting these three aspects, it is argued that The Last of Us portrays bodily boundaries as precarious, and that it presents violence, technology and infectious disease as callingcards for moving beyond anthropocentric views of corporeality; of conceptualizing the human body as machine-like and inevitably more-than-human.

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19

Callens, Johan, and Johan Callens. "Grens/gevallen." Documenta 12, no. 4 (April 14, 2019): 215–48. http://dx.doi.org/10.21825/doc.v12i4.10717.

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20

Palmer, J. D., and J. A. Page. "Gobsmacking grins'." British Dental Journal 171, no. 1 (July 1991): 28–29. http://dx.doi.org/10.1038/sj.bdj.4807595.

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21

Głąbińska, Dominika. "Svenskhet som gräns." Studia Scandinavica, no. 1 (21) (December 17, 2017): 175–90. http://dx.doi.org/10.26881/ss.2017.21.12.

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The goal of the article “Svenskhet som gräns, Swedishness as a border” is to present problems with the concept of Swedishness from another perspective than it has been discussed in the Swedish media. The article analyses responses from Swedish political parties with regard to “Swedishness” and democracy, and it provides an insight to the contemporary situation in Sweden.

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Karlsson, Gunnel, Beata Losman, and Louise Lönnroth. "Vid kunskapens gräns." Tidskrift för genusvetenskap 4, no. 2 (June 27, 2022): 43–50. http://dx.doi.org/10.55870/tgv.v4i2.5761.

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Ett viktigt metodproblem för kvinnoforskare är att källmaterialet ofta är utformat av och för män. Ett exempel är skattelängderna där endast mannens namn sattes ut medan hustru, barn och tjänstefolk angavs som streck i olika kolumner. Författarna visarfrån sin egen forskning att förvaltningsmaterial om kvinnor finns men måste behandlas med stor försiktighet.

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23

van Oers, Sanne. "Boven de grens." Advocatenblad 102, no. 5 (June 2022): 3. http://dx.doi.org/10.5553/ab/0165-13312022102005001.

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Jacobs, Evelien. "DE GRENS OVERGAAN." TvPO 17, no. 1 (January 31, 2022): 50. http://dx.doi.org/10.1007/s12503-022-0919-1.

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Eker, Mark, and Henk Van Houtum. "Ontwerp de grens." AGORA Magazine 28, no. 4 (September 1, 2012): 6–8. http://dx.doi.org/10.21825/agora.v28i4.2396.

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Kleinherenbrink, Arjen, and Simon Gusman. "Over de grens." Algemeen Nederlands Tijdschrift voor Wijsbegeerte 106, no. 3 (September 21, 2014): 257–61. http://dx.doi.org/10.5117/antw2014.3.klei.

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. "Over de grens." Supervisie en Coaching 25, no. 2 (June 2008): 123–30. http://dx.doi.org/10.1007/bf03099288.

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. "Over de grens." Maatwerk 7, no. 6 (December 2006): 270–71. http://dx.doi.org/10.1007/bf03070749.

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Berenbaum, May. "Frass-Eating Grins." American Entomologist 49, no. 3 (2003): 132–33. http://dx.doi.org/10.1093/ae/49.3.132.

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ten Voorde, Jeroen. "Over de grens." PROCES 96, no. 4 (August 2017): 265–66. http://dx.doi.org/10.5553/proces/016500762017096004001.

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31

Konings, Jos. "Over de grens." Tijdschrift voor VerpleeghuisGeneeskunde 34, no. 2 (February 2009): 42. http://dx.doi.org/10.1007/bf03081357.

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32

Song, Lu-Kai, Guang-Chen Bai, Cheng-Wei Fei, and Jie Wen. "Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling." Advances in Materials Science and Engineering 2018 (October 16, 2018): 1–16. http://dx.doi.org/10.1155/2018/3469465.

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To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure.

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33

Chen, Chi-Kan. "Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections." Statistical Applications in Genetics and Molecular Biology 20, no. 4-6 (December 1, 2021): 121–43. http://dx.doi.org/10.1515/sagmb-2020-0054.

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Abstract The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.

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34

Zhou, Xin, and Xiaodong Cai. "Inference of differential gene regulatory networks based on gene expression and genetic perturbation data." Bioinformatics 36, no. 1 (July 2, 2019): 197–204. http://dx.doi.org/10.1093/bioinformatics/btz529.

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Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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35

Herawati, Sri, and M. Latif. "Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network." JURNAL INFOTEL - Informatika Telekomunikasi Elektronika 8, no. 2 (November 14, 2016): 132. http://dx.doi.org/10.20895/infotel.v8i2.124.

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Abstract—The method of time series suitable for use when it checks each data patterns systematically and has many variables, such as in the case of crude oil prices. One study that utilizes the methods of time series is the integration between Ensemble Empirical Mode Decomposition (EEMD) and neural network algorithms based on Polak-Ribiere Conjugate Gradient (PCG). However, PCG requires setting free parameters in the learning process. Meanwhile, the appropriate parameters are needed to get accurate forecasting results. This research proposes the integration between EEMD and Generalized Regression Neural Network (GRNN). GRNN has advantages, such as: does not require any parameter settings and a quick learning process. For the evaluation, the performance of the method EEMD-GRNN compared with GRNN. The experimental results showed that the method EEMD-GRNN produce better forecasting of GRNN. Keywords-Forecasting crude oil price; EEMD;GRNN.

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Yang, Pengfei, Xianbo Sun, Li Zhu, Yuhan Wu, and Baofu Dai. "Load Identification Method Based on ISMA-GRNN." Mathematical Problems in Engineering 2022 (March 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/8056696.

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The noninvasive load monitoring method carries out load identification after event detection and feature extraction of load data. At present, nonload intrusive load monitoring faces the problems of low load identification accuracy and long load identification time. In order to solve these problems, a load identification method based on the improved slime mould algorithm-generalized regression neural network (ISMA-GRNN) is proposed. Firstly, by adding mutation operation in slime mould algorithm (SMA) position update, the global optimization ability of SMA is improved. Then, the improved slime mould algorithm (ISMA) is used to optimize the smoothing factor of GRNN and find the best smoothing factor. Finally, the best smoothing factor is input into GRNN for load identification, and the load identification results are output. To measure the effect of load identification, load identification precision, load identification accuracy, and load identification time are used as evaluation indicators. The simulation results show that compared with HHO-GRNN and WOA-GRNN, the load identification time of SMA-GRNN is greatly shortened, but the results are not satisfactory. On the basis of SMA-GRNN, ISMA-GRNN has significantly improved the accuracy and precision of load identification. In conclusion, ISMA-GRNN can better adapt to the load identification of multiple electrical equipment scenes.

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37

Zhao, Huiru, and Sen Guo. "Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/217630.

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Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country. In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting. The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems. Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm optimization algorithm and cultural algorithm). Taking China’s annual energy consumption as the empirical example, the effectiveness of this proposed PSOCA-GRNN model was proved. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, GRNN model optimized by PSO (PSO-GRNN), discrete grey model (DGM (1, 1)), and ordinary least squares linear regression (OLS_LR) model.

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38

Wu, Cheng-Wei, Wu-Xing Zhou, Guofeng Xie, Xue-Kun Chen, Dan Wu, and Zhi-Qiang Fan. "Enhancement of thermoelectric performance in graphenylene nanoribbons by suppressing phonon thermal conductance: the role of phonon local resonance." Nanotechnology 33, no. 21 (February 28, 2022): 215402. http://dx.doi.org/10.1088/1361-6528/ac5288.

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Abstract Based on the method of non-equilibrium Green’s function, we investigate the thermal transport and thermoelectric properties of graphenylene nanoribbons (GRNRs) with different width and chirality. The results show that the thermoelectric (TE) performance of GRNRs significantly increases with decreasing ribbon width, which stems from the reduction of thermal conductance. In addition, by changing the ribbon width and chirality, the figure of merit ( Z T ) can be controllably manipulated and maximized up to 0.45 at room temperature. Moreover, it is found that the Z T value of GRNRs with branched structure can reach 1.8 at 300 K and 3.4 at 800 K owing to the phonon local resonance. Our findings here are of great importance for thermoelectric applications of GRNRs.

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39

Nivina, Aleksandra, Sur Herrera Paredes, Hunter B. Fraser, and Chaitan Khosla. "GRINS: Genetic elements that recode assembly-line polyketide synthases and accelerate their diversification." Proceedings of the National Academy of Sciences 118, no. 26 (June 23, 2021): e2100751118. http://dx.doi.org/10.1073/pnas.2100751118.

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Assembly-line polyketide synthases (PKSs) are large and complex enzymatic machineries with a multimodular architecture, typically encoded in bacterial genomes by biosynthetic gene clusters. Their modularity has led to an astounding diversity of biosynthesized molecules, many with medical relevance. Thus, understanding the mechanisms that drive PKS evolution is fundamental for both functional prediction of natural PKSs as well as for the engineering of novel PKSs. Here, we describe a repetitive genetic element in assembly-line PKS genes which appears to play a role in accelerating the diversification of closely related biosynthetic clusters. We named this element GRINS: genetic repeats of intense nucleotide skews. GRINS appear to recode PKS protein regions with a biased nucleotide composition and to promote gene conversion. GRINS are present in a large number of assembly-line PKS gene clusters and are particularly widespread in the actinobacterial genus Streptomyces. While the molecular mechanisms associated with GRINS appearance, dissemination, and maintenance are unknown, the presence of GRINS in a broad range of bacterial phyla and gene families indicates that these genetic elements could play a fundamental role in protein evolution.

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40

Kim, Hyobin, and Hiroki Sayama. "How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations." Artificial Life 24, no. 2 (May 2018): 85–105. http://dx.doi.org/10.1162/artl_a_00262.

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Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.

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41

He, Xiang Yu, and Shang Hong He. "Fault Detection of Excavator’s Hydraulic System Using Dynamic General Regression Neural Network." Applied Mechanics and Materials 48-49 (February 2011): 511–14. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.511.

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In order to improve reliability of excavator’s hydraulic system, a fault detection approach based upon dynamic general regression neural network (GRNN) approach was proposed. Dynamic GRNN is an extension of GRNN, which could effectively caputure the dynamic behavior of the nonlinear process. With this approach, normal samples were used as training data to develop a dynamic GRNN model in the first step. Secondly, this dynamic GRNN model performed as a fault determinant of the test fault. Experimental faults were used to validate the approach. Experimental results show that the proposed fault detection approach could effectively applied to the excavator’s hydraulic system.

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42

Ristevski, Blagoj. "A survey of models for inference of gene regulatory networks." Nonlinear Analysis: Modelling and Control 18, no. 4 (October 25, 2013): 444–65. http://dx.doi.org/10.15388/na.18.4.13972.

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In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.

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43

Liu, M., and Z. H. Sun. "Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition." Computer Optics 45, no. 2 (April 2021): 296–300. http://dx.doi.org/10.18287/2412-6179-co-789.

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With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

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44

Wang, Liang, Yang Xia, and Yichun Lu. "A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices." Computational Intelligence and Neuroscience 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/4952215.

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It is hard to forecasting oil future prices accurately, which is affected by some nonlinear, nonstationary, and other chaotic characteristics. Then, a novel GA-SVR-GRNN hybrid deep learning algorithm is put forward for forecasting oil future price. First, a genetic algorithm (GA) is employed for optimizing parameters regarding the support vector regression machine (SVR), and the GA-SVR model is used to forecast oil future price. Further, a generalized regression neural network (GRNN) model is built for the residual series for forecasting. Finally, we obtain the predicted values of the oil future price series forecasted by the GA-SVR-GRNN hybrid deep learning algorithm. According to the simulation, the GA-SVR-GRNN hybrid deep learning algorithm achieves lower MSE, RMSE, MAE, and MAPE relative to the GRNN, GA-SVR, and PSO-SVR models, indicating that the proposed GA-SVR-GRNN hybrid deep learning algorithm can fully reveal the prediction advantages of the GA-SVR and GRNN models in the nonlinear space and is a more accurate and effective method for oil future price forecasting.

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45

Mi, Han, Wenlong Guo, Lisi Liang, Hongyue Ma, Ziheng Zhang, Yanli Gao, and Linbo Li. "Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm." Materials 15, no. 23 (December 2, 2022): 8608. http://dx.doi.org/10.3390/ma15238608.

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The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.

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46

Vink, Caroline. "Kinderbescherming over de grens." Justitiële verkenningen 45, no. 6 (December 2019): 42–50. http://dx.doi.org/10.5553/jv/016758502019045006004.

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47

Droogleever Fortuyn, Sabine. "Groeien over de grens." Advocatenblad 96, no. 7 (November 2016): 36–37. http://dx.doi.org/10.5553/ab/01651331016096007012.

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48

van Thull, Geert. "Kijkje over de grens." Management Kinderopvang 28, no. 2 (March 18, 2022): 32–35. http://dx.doi.org/10.1007/s41190-022-1034-9.

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49

Zagema, Bertram. "Trek de groene grens." AGORA Magazine 15, no. 5 (December 1, 1999): 8–9. http://dx.doi.org/10.21825/agora.v15i5.9399.

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50

van der Waerden, José. "De grens van zelfredzaamheid." Zorg + Welzijn 23, no. 1-2 (January 2017): 18–19. http://dx.doi.org/10.1007/s41185-017-0008-4.

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