Functional networks and applications: A survey

Zhou, Guo; Zhou, Yongquan; Huang, Huajuan; Tang, Zhonghua

doi:10.1016/j.neucom.2018.04.085

Cited by 15 publications

(5 citation statements)

References 91 publications

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“…The weather condition is considered based on the hot, cold, and moderate climate situations in a different region. The best models are represented based on the best performance of the Gaussian Process Regression (GPR), Supporting Vector Regression (SVR), and decision tree algorithm (Zhou et al, 2019;Ahmad et al, 2018;Fathi et al, 2020).…”

Section: Machine Learningmentioning

confidence: 99%

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

et al. 2023

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Section: Machine Learningmentioning

confidence: 99%

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

et al. 2023

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“…a group of direct connections that join units in the input or intermediate layers with neurons, and neurons with intermediate or output units. When choosing a FN, there are two aspects to take into account: first, use the functions' family; second, choose the functions' items from the family [39]. Different functions 'family were used in this study such as functional network forward-backward (FNFBM), functional network backward-forward (FNBFM), FNFSM Functional network forward-selection method, FNESM Functional network exhaustive-search method, FNBEM Functional network backward-elimination method.…”

Section: Functional Network Model Developmentmentioning

confidence: 99%

Different Machine Learning Approaches to predict Gas Deviation Factor (Z-factor)

Elsayed

Alsabaa

Salem

2023

Journal of Petroleum and Mining Engineering

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The gas compressibility factor indicates the gas deviation from ideal gas behavior. Accurate values of gas compressibility factor affect the estimation of reservoir fluid properties, the initial gas in place, and the natural gas production and transportation process. Gas compressibility factor can be estimated in labs; however, this method is expensive and time-consuming. Due to these challenges, numerous studies created various empirical correlations depending on the results of the equation of state. The Standing and Katz chart is regarded as a standard for estimating gas compressibility factor. Many studies proposed approaches and correlations to fit this chart, however some did not cover the entire range of data, others provided implicit methods taking long time for calculation or faced high errors out of the data range. In this study, Support Vector Machine, Radial Basis Function, and Functional Network as machine learning approaches were implemented to predict the gas compressibility factor, based on 5490 data set of Standing and Katz chart. 70% of the data set was implemented in the training process and 30% in the testing process. The data set included pseudo-reduced pressure and pseudo-reduced temperature as inputs and Zfactor as an output. Different training functions were examined for each method for the best approach optimization. In addition, machine learning best approach was compared with other correlations. The best results in this work were obtained from Radial Basis Function with 0.14 average absolute percentage error and 0.99 correlation coefficient. The developed machine learning approach performed better than the examined correlations.

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“…The learning process in FN involves both parametric and structural. Parametric is the estimation of the neural function while structural is based on the topology of the network [14]. FN has the following components (a) input layer, an output layer, and one or several layers of processing unit.…”

Section: E Functional Networkmentioning

confidence: 99%

A Comprehensive Review of Soft Computing Models for Permeability Prediction

Almutairi

2021

IEEE Access

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Functional networks and applications: A survey

Cited by 15 publications

References 91 publications

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

Different Machine Learning Approaches to predict Gas Deviation Factor (Z-factor)

A Comprehensive Review of Soft Computing Models for Permeability Prediction

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