A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

Lashkenari, Mohammad Soleimani; Bagheri, Mohammad; Tatar, Afshin; Rezazadeh, Hadi; Inc, Mustafa

doi:10.1007/s00521-023-08256-y

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…On the other hand, RBF achieves an accuracy rate of up to 76.43% in only 56 s. As a result, in terms of both time and accuracy, it would be more appealing to propose the use of RBF, particularly in big data research. Recent research [53][54][55] comparing MLP and RBF has also demonstrated that RBF is more efficient. RBF networks are generally faster to train and more computationally efficient than other types of neural networks.…”

Section: Discussionmentioning

confidence: 99%

Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

Bozkurt,

Şeker

2023

Sustainability

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The classification of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) is essential for promoting sustainable tourism and ensuring the long-term conservation of cultural and natural heritage sites. Therefore, two commonly used techniques for classification problems, multilayer perceptron (MLP) and radial basis function (RBF) neural networks, were utilized to define the pros and cons of their applications. Then, according to the findings, both correlation attribute evaluator (CAE) and relief attribute evaluator (RAE) identified the region and date of inscription as the most prominent features in the classification of UNESCO WHS. As a result, a trade-off condition arises when classifying a large dataset for sustainable tourism between MLP and RBF regarding evaluation time and accuracy. MLP achieves a slightly higher accuracy rate with higher processing time, while RBF achieves a slightly lower accuracy rate but with much faster evaluation time.

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Section: Discussionmentioning

confidence: 99%

Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

Bozkurt,

Şeker

2023

Sustainability

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“…The prediction method based on radial basis function neural networks has been widely studied and applied in other fields [8][9][10][11][12]. Lashkenari et al [13] developed a radial basis function neural network prediction model for predicting the viscosity of Iranian crude oil that has universality and accuracy. Luo et al [14] proposed a fast method for predicting the fatigue life of automotive wheels based on radial basis function neural networks combined with orthogonal decomposition, which has ideal accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Yu,

Zheng,

Zhao

2023

Electronics

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A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data characteristics and distribution differences on the prediction model. Finally, based on different prediction objectives, time-driven and data-driven calibration data prediction models are constructed using RBF neural networks. Through simulation analysis, it is shown that an RBF neural network is superior to a BP neural network in predicting clock tester calibration data, and time-driven prediction accuracy is superior to data-driven prediction accuracy. Moreover, the prediction error and mean square error of both prediction models are on the order of 10−9, meeting the prediction accuracy requirements.

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Understanding evolving user choices: a neural network analysis of TAXI and ride-hailing services in Barcelona

Guillén-Pujadas,

Vizuete-Luciano,

Alaminos

et al. 2023

Soft Comput

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Urban mobility stands as a fundamental element worthy of consideration by both society and its leaders. Often, decisions in this realm are made by governing figures without duly factoring in the preferences and needs of citizens. In our study, we delve into the changes that have unfolded within Barcelona from the standpoint of its users. The primary aim of this article is to observe the preferences that these users hold regarding the array of mobility options available to them in the urban environment. To this end, we’ve incorporated various aspects of significant relevance and contemporary presence in today’s society, such as mobility and sustainability, focusing specifically on the two most frequently used types of commercial passenger vehicles (CPV) within urban contexts: taxis and ride-hailing services. To gather the necessary sample data, a survey was conducted with a significance level of 95%. Following an exhaustive examination of the existing literature surrounding these concepts, we proceeded with the analysis of the sample using neural networks. The outcomes garnered encompass user receptiveness to the technological evolution shaping the sector, the utilization of mobile applications, a predisposition to opt for fixed tariffs, and the pivotal role of new blockchain-based technologies (NFTs and fan tokens) in influencing user decision-making.

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A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model

Cited by 6 publications

References 40 publications

Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Understanding evolving user choices: a neural network analysis of TAXI and ride-hailing services in Barcelona

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