Systems Modeling Using Deep Elman Neural Network-Reference-Cited by-同舟云学术

Systems Modeling Using Deep Elman Neural Network

Published:2019-04-10 Issue:2 Volume:9 Page:3881-3886
ISSN:1792-8036
Container-title:Engineering, Technology & Applied Science Research
language:
Short-container-title:Eng. Technol. Appl. Sci. Res.

Author:

Belhaj Salah L.,Fourati F.

Abstract

In this paper, the modeling of complex systems using deep Elman neural network architecture is improved. The emphasis is to retrieve better deep Elman structure that emulates perfectly such dynamic systems. To achieve this goal, sigmoid activation functions in the hidden and output layers nodes are chosen and data files on considered systems for modeling and validation steps are given. Simulation results prove the ability and the efficiency of a deep Elman neural network with two hidden layers in this task.

Publisher

Engineering, Technology & Applied Science Research

Link

http://etasr.com/index.php/ETASR/article/download/2455/pdf

Reference35 articles.

1. J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural Networks, Vol. 61, pp. 85-117, 2015

2. N. Majumder, S. Poria, A. Gelbukh, E. Cambria, “Deep learning-based document modeling for personality detection from text”, IEEE Intelligent Systems, Vol. 32, pp. 74-79, 2017

3. Z. Jiang, L. Li, D. Huang, L. Lin, “Training word embeddings for deep learning in biomedical text mining tasks”, IEEE International Conference on Bioinformatics and Biomedicine, Washington, DC, USA, November 9-12, 2015

4. L. Deng, G. Hinton, B. Kingsbury, “New types of deep neual network learning for speech recognition and related applications: an overview”, IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, May 26-31, 2013

5. D. Chen, B. K. W. Mak, “Multitask learning of deep neural networks for low-resource speech recognition”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 23, No. 7, pp. 1172-1183, 2015

Cited by 9 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. Predicting the Number of Software Faults using Deep Learning;Engineering, Technology & Applied Science Research;2024-04-02

2. A review of technological developments in modern farming: Intelligent greenhouse systems;THE 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT, EPIDEMIOLOGY AND INFORMATION SYSTEM (ICENIS) 2021: Topic of Energy, Environment, Epidemiology, and Information System;2023

3. A Comparative Study of SSA-BPNN, SSA-ENN, and SSA-SVR Models for Predicting the Thickness of an Excavation Damaged Zone around the Roadway in Rock;Mathematics;2022-04-18

4. Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy;Engineering, Technology & Applied Science Research;2022-04-09

5. Recurrent Neural Network-based Path Planning for an Excavator Arm under Varying Environment;Engineering, Technology & Applied Science Research;2021-06-15