A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting-Reference-Cited by-同舟云学术

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A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting

Published:2024-04 Issue: Volume:289 Page:111481
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Zhou Kun^ORCID,Oh Sung-Kwun^ORCID,Pedrycz Witold,Qiu Jianlong,Seo Kisung

Funder

Natural Science Foundation of Shandong Province

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference51 articles.

1. Enhancing the reliability of image classification using the intrinsic features;Lu;Knowl. Based Syst.,2023

2. Multi-source domain adaptation method for textual emotion classification using deep and broad learning;Peng;Knowl. Based Syst.,2023

3. Deep reinforcement learning based on transformer and U-Net framework for stock trading;Yang;Knowl. Based Syst.,2023

4. Self-organizing Methods in modeling: GMDH Type Algorithms;Farlow,2020

5. The review of problems solvable by algorithms of the group method of data handling (GMDH);Ivakhnenko;Pattern Recognit. Image Anal.,1995

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing the performance of deep learning models with fuzzy c-means clustering;Knowledge and Information Systems;2024-08-24

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