Temat: MLP - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Prediction of the compressive strength of environmentally friendly concrete using artificial neural network
Autorzy:: Kulisz, Monika
Kujawska, Justyna
Aubakirova, Zulfiya
Zhairbaeva, Gulnaz
Warowny, Tomasz
Tematy:: ANN
compressive strength
RCA
MLP
RBF; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/38432692.pdf Link otwiera się w nowym oknie
Opis:: The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A deep learning model for electricity demand forecasting based on a tropical data
Autorzy:: Adewuyi, Saheed A.
Aina, Segun
Oluwaranti, Adeniran I.
Tematy:: Electricity Demand Forecasting
STLF
Deep Learning Techniques
LSTM
CNN
MLP
prognozowanie zapotrzebowania na energię elektryczną
techniki głębokiego uczenia; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/118123.pdf Link otwiera się w nowym oknie
Opis:: Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.
Dostawca treści:: Biblioteka Nauki

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