From Mean and Median Income to the Most Adequate Way of Taking Inequality into Account

Kreinovich, Vladik; Nguyen, Hung T.; Ouncharoen, Rujira

doi:10.1007/978-3-319-13449-9_5

Cited by 4 publications

(4 citation statements)

References 23 publications

(11 reference statements)

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“…We opted for the SMAPE as the preferred metric over mean absolute percentage error (MAPE) due to its ability to handle situations with zero values in a more robust and balanced manner. 31 An update of the new recipe is then carried out iteratively using a global optimizer as explained in section 2.7. As soon as the two descriptor sets are sufficiently similar, the process is stopped, and a final recipe is proposed.…”

Section: Reaction Chemistry and Engineeringmentioning

confidence: 99%

Computer aided recipe design: optimization of polydisperse chemical mixtures using molecular descriptors

MacKenzie,

Schneider,

Meyer

et al. 2024

React. Chem. Eng.

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Section: Reaction Chemistry and Engineeringmentioning

confidence: 99%

Computer aided recipe design: optimization of polydisperse chemical mixtures using molecular descriptors

MacKenzie,

Schneider,

Meyer

et al. 2024

React. Chem. Eng.

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“…where MAPE provides a percentage error, which simplifies interpreting and comparing forecasting models' accuracy. c) Symmetric Mean Absolute Percentage Error (sMAPE): [26] measures the average percentage difference between the predicted and actual values and is symmetrical since it treats positive and negative errors equally. In our evaluation of the…”

Section: Prediction Metricsmentioning

confidence: 99%

Machine Learning Based Resource Utilization Prediction in the Computing Continuum

Bauer,

Mehran,

Prodan

et al. 2023

2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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This paper presents UtilML, a novel approach for tackling resource utilization prediction challenges in the computing continuum. UtilML leverages Long-Short-Term Memory (LSTM) neural networks, a machine learning technique, to forecast resource utilization accurately. The effectiveness of UtilML is demonstrated through its evaluation of data extracted from a real GPU cluster in a computing continuum infrastructure comprising more than 1800 computing devices. To assess the performance of UtilML, we compared it with two related approaches that utilize a Baseline-LSTM model. Furthermore, we analyzed the LSTM results against User-Predicted values provided by GPU cluster owners for task deployment with estimated allocation values. The results indicate that UtilML outperformed user predictions by 2% to 27% for CPU utilization prediction. For memory prediction, UtilML variants excelled, showing improvements of 17% to 20% compared to user predictions.

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“…All simulations were written in MATLAB R2016b and run on a PC with a clock speed of 2.10 GHZ and 8 GB of memory in a Microsoft Windows 7 environment, and the rootmean-square error (RMSE) and average percentage error (APE) are introduced in this study [35],…”

Section: Simulation Analysismentioning

confidence: 99%

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

Zhang

Wang

2022

Applied Sciences

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In this paper, an improved self-organizing fuzzy neural network (SOFNN-CA) based on a clustering algorithm is proposed for nonlinear systems modeling in industrial processes. In order to reduce training time and increase training speed, we combine offline learning and online identification. The unsupervised clustering algorithm is used to generate the initial centers of the network in the offline learning phase, and, in the self-organizing phase of the system, the Mahalanobis distance (MD) index and error criterion are adopted to add neurons to learn new features. A new density potential index (DPI) combined with neuron local field potential (LFP) is designed to adjust the neuron width, which further improves the network generalization. The similarity index calculated by the Gaussian error function is used to merge neurons to reduce redundancy. Meanwhile, the convergence of SOFNN-CA in the case of structural self-organization is demonstrated. Simulations and experiments results show that the proposed SOFNN-CA has a more desirable modeling accuracy and convergence speed compared with SOFNN-ALA and SOFNN-AGA.

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From Mean and Median Income to the Most Adequate Way of Taking Inequality into Account

Cited by 4 publications

References 23 publications

Computer aided recipe design: optimization of polydisperse chemical mixtures using molecular descriptors

Computer aided recipe design: optimization of polydisperse chemical mixtures using molecular descriptors

Machine Learning Based Resource Utilization Prediction in the Computing Continuum

Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm

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