A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

Bani-Hani, Dana; Khasawneh, Mohammad T.

doi:10.1016/j.eswa.2019.06.018

Cited by 20 publications

(7 citation statements)

References 30 publications

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“…General Regression Neural Network (GRNN), as a branch of Radial Basis Neural Network, was proposed by American scholar Dr. Sprecht (Kamel et al, 2021). It does not require an iterative method like the back propagation learning algorithm to perform result simulation, and it has the ability to approximate arbitrary functions directly from the training data input and output data sets (Bani-Hani and Khasawneh, 2019). The GRNN network structure is consisted of an input layer, a pattern layer, a summation layer and an output layer.…”

Section: Grnnmentioning

confidence: 99%

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

JIN

Zhu

2022

JTST

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Application of machine learning algorithms for the predictionof flame temperature in small-scale burner fueled with ethanol-diesel fuel blends IntroductionAs a light petroleum product, diesel fuel is widely used in ships, large-scale vehicles, and locomotives in railway. However, contaminants such as oxides of nitrogen (NOx) and particulate matter (PM) are generated with the use of diesel fuel. It was found that diesel fuel can be partially replaced by oxygenated fuels, reducing contaminant emissions from combustion. Consequently, many researchers have devoted their attention to the study of oxygenated fuels, such as methanol, ethanol and biodiesel (Hansen et al., 2005;Moon et al., 2010;Yao et al., 2010). Ethanol contains oxygen in 34.7wt% and its required air is theoretically nearly half as diesel when burned (Lu et al., 2021). Therefore, diesel fuel blended with ethanol can make combustion more fully, which reduces soot emissions.Ethanol-diesel fuel blends requires appropriate equipment to achieve efficient combustion. The small-scale burner is the key equipment of small-electromechanical system and this burner has the advantage of high energy density. However, in the small scale, many new problems and challenges arise, such as incomplete combustion, unstable flame, and large heat loss (

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Section: Grnnmentioning

confidence: 99%

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

JIN

Zhu

2022

JTST

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“…Finally, a number of Machine Learning models are used to predict data, specifically for time-series processes, and in particular Neural Network variants have presented encouraging results for classification problems, e.g., [11,12]. In this study, after fitting models of LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network) and RBN (Radial Basis Network), we present the results for the model found to best fit the data sample in our simulations, that is RBN, and in particular GRNN (Generalised Regression Neural Networks).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021

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Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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“…At last, GRNN converges to the optimal regression surface with more sample size. However, its disadvantage is that the learning sample size is small, the parameters of the model are missing, and the smoothing factor is not easy to select [11,12]. BPNN has strong nonlinear mapping ability, high self-learning and self-adaptive ability, but the network tends to converge to different local extremums, and the convergence speed is relatively slow [13,14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of compressive strength based on visualization of pellet microstructure data

Yang

Zhuansun

2021

J. Iron Steel Res. Int.

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In recent years, with the wide application of image data visual extraction technology in the field of industrial engineering, the development of industrial economy has reached a new situation. To explore the interaction between the pellet microstructure and compressive strength, firstly, the pellet microstructure needed for the experiment was obtained using a Leica DM4500P microscope. The area proportions of hematite, calcium ferrite, magnetite, calcium silicate and pore in pellet microstructure were extracted by visual extraction technology of image data. Moreover, the relationship between the area proportions of mineral components and compressive strength was established by backpropagation neural network (BPNN), generalized regression neural network (GRNN) and beetle antennae search-generalized regression neural network (BAS-GRNN) algorithms, which proves that the pellet microstructure can be used as the prediction standard of compressive strength. The errors of BPNN and BAS-GRNN are 5.13% and 3.37%, respectively, both of which are less than 5.5%. Therefore, through data visualization, we are able to discuss the connection between various components of pellet microstructure and compressive strength and provide new research ideas for improving the compressive strength and metallurgical performance of pellet.

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A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

Cited by 20 publications

References 30 publications

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

Prediction of compressive strength based on visualization of pellet microstructure data

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