Determining optimal neural network architecture using regression methods

Tej, Mohamed Lafif; Holban, Ştefan

doi:10.1109/daas.2018.8396093

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…This set of particles is called a swarm, and eventually this swarm will move towards the best solution. One method that can be practically applied is data mining techniques, including clustering and regression [31], [32]. However, in this study, it is less explained how the relationship between the number of clusters and feature extraction are utilized in the machine learning process.…”

Section: Related Workmentioning

confidence: 93%

A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

2020

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Section: Related Workmentioning

confidence: 93%

A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

2020

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“…The literature on the configuration of a neural network is extensive and the possibilities are innumerable [32][33][34][35]; therefore, the search for this configuration is usually delimited to a grid. This tool establishes a series of intervals that define the parameters to be evaluated.…”

Section: Lstm Neural Networkmentioning

confidence: 99%

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

Pensado-Mariño

Garrido²,

Eguía

et al. 2021

Sustainability

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The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.

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Determining the Number of Hidden Layers in Neural Network by Using Principal Component Analysis

Santoso

Surendro

2019

Advances in Intelligent Systems and Computing

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Determining optimal neural network architecture using regression methods

Cited by 5 publications

References 26 publications

A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

Determining the Number of Hidden Layers in Neural Network by Using Principal Component Analysis

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