Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction-Reference-Cited by-同舟云学术

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Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction

Published:2022-02 Issue: Volume:108 Page:104570
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Salloom Tony^ORCID,Kaynak Okyay^ORCID,Yu Xinbo,He Wei

Funder

National Natural Science Foundation of China

University of Science and Technology Beijing Shunde Graduate School

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference57 articles.

1. A new hybrid financial time series prediction model;Alhnaity;Eng. Appl. Artif. Intell.,2020

2. Committee machines for hourly water demand forecasting in water supply systems;Ambrosio;Math. Probl. Eng.,2019

3. Short-term water demand forecasting using machine learning techniques;Antunes;J. Hydroinform.,2018

4. Evaluation of peak water demand factors in puglia (Southern Italy);Balacco;Water (Switzerland),2017

5. Multi-step-ahead time series prediction using multiple-output support vector regression;Bao;Neurocomputing,2014

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