Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

Liu, Shilin; Yu, Guangbin; Kim, Youngchul

doi:10.4018/ijitwe.339186

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Radial basis function (RBF) networks are used for function approximation problems in various applications, including function approximation, time series prediction, classification tasks and control [27,28]. This type of artificial neural network uses radial basis functions as activation functions, and it consists of an input layer, a hidden layer and an output layer [29].…”

Section: Related Workmentioning

confidence: 99%

Magnetic Characterization of MR Fluid by Means of Neural Networks

Kowol,

Lo Sciuto,

Brociek

et al. 2024

Electronics

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Magnetorheological and electrorheological fluids manifest a change in rheological behavior when subjected to a magnetic or electric field, respectively, such that they require electrical and magnetic characterization. In this paper, a simple and accurate mathematical model based on a small number of parameters provides the relative magnetic permeability of magnetorheological fluids as a function of the applied magnetic field. Furthermore, for the testing and magnetic characterization of magnetorheological fluids, a new metering equipment setup is implemented. Starting with the achieved experimental data, the mathematical relation μr=f(B) is represented by means of a radial basis function neural network, with neurons having a Gaussian activation function; by means of post-training pruning procedures, the trained neural network is applied using the proposed data. Therefore, the obtained mathematical relation μr=f(B) is in good agreement with the experimental data, with an approximate error of 8%.

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Section: Related Workmentioning

confidence: 99%

Magnetic Characterization of MR Fluid by Means of Neural Networks

Kowol,

Lo Sciuto,

Brociek

et al. 2024

Electronics

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“…Furthermore, neural network models exhibit good generalization capabilities and strong fault tolerance. Through appropriate training, neural networks can generalize to unseen data and tolerate noise and missing values in input data to a certain extent, allowing them to address problems that traditional linear models cannot handle [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Coordinated Development of Digital Economy, Green Technology Innovation, and Ecological Environment Quality—A Case Study of China

Li,

Deng,

et al. 2024

Sustainability

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Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection.

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Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

Cited by 2 publications

References 25 publications

Magnetic Characterization of MR Fluid by Means of Neural Networks

Magnetic Characterization of MR Fluid by Means of Neural Networks

Research on the Coordinated Development of Digital Economy, Green Technology Innovation, and Ecological Environment Quality—A Case Study of China

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