Deep learning controller for nonlinear system based on Lyapunov stability criterion
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
If the conditions described in Eqs. (20) and (21) are both satisfied, then the system is considered to be asymptotically stable.
“If the conditions described in Eqs. (20) and (21) are both satisfied, then the system is considered to be asymptotically stable [27, 28].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [27]
Cites
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Identification and control of dynamical systems using neural networks
Narendra, K. S., & Parthasarathy, K. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80202 · Cited by 8,031 · see citing works
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,111 · see citing works
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1× · see citing works
“Based on this approach, contrastive divergence (CD) was proposed by Hinton for learning RBM [41, 42].”
BackgroundA survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852 · see citing works
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,180 · see citing works
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390290 · Cited by 847 · see citing works
Data-driven soft sensor development based on deep learning technique
Shang, C., Yang, F., Huang, D., & Lyu, W. (2014) · Journal of Process Control · doi:10.1016/j.jprocont.2014.01.012 · Cited by 551 · see citing works
McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 502 · see citing works
Deep learning and its application in geochemical mapping
Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019) · Earth-Science Reviews · doi:10.1016/j.earscirev.2019.02.023 · Cited by 435 · see citing works
Understanding Emotions in Text Using Deep Learning and Big Data
Chatterjee, A., Gupta, U., Chinnakotla, M. K., Srikanth, R., Galley, M., & Agrawal, P. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.029 · Cited by 395 · see citing works
The present and future of deep learning in radiology
Saba, L., Biswas, M., Kuppili, V., Cuadrado Godia, E., Suri, H. S., Edla, D. R., Omerzu, T., Laird, J. R., Khanna, N. N., Mavrogeni, S., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.038 · Cited by 353 · see citing works
Applying neural networks to on-line updated PID controllers for nonlinear process control
Chen, J., & Huang, T.-C. (2003) · Journal of Process Control · doi:10.1016/s0959-1524(03)00039-8 · Cited by 243 · see citing works
PID controller design of nonlinear systems using an improved particle swarm optimization approach
Chang, W., & Shih, S.-P. (2010) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2010.01.005 · Cited by 188 · see citing works
On Adaptive Learning Rate That Guarantees Convergence in Feedforward Networks
Behera, L., Kumar, S., & Patnaik, A. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.878121 · Cited by 169 · see citing works
Deep learning for identifying radiogenomic associations in breast cancer
Zhu, Z., AlBadawy, E. A., Saha, A., Zhang, J., Harowicz, M. R., & Mazurowski, M. A. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.018 · Cited by 167 · see citing works
Kumar, R., Srivastava, S., & Gupta, J. R. P. (2017) · ISA Transactions · doi:10.1016/j.isatra.2017.01.022 · Cited by 102 · see citing works
A greenhouse control with feed-forward and recurrent neural networks
Fourati, F., & Chtourou, M. (2007) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2007.06.001 · Cited by 102 · see citing works
Modeling of nonlinear system based on deep learning framework
Xiangjun, J., Shao, J., Zhang, X., et al. (2015) · Nonlinear Dynamics · doi:10.1007/s11071-015-2571-6 · Cited by 101 · see citing works
A deep learning based multitask model for network-wide traffic speed prediction
Zhang, K., Zheng, L., Liu, Z., & Jia, N. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.10.097 · Cited by 96 · see citing works
Kayacan, E., Kayacan, E., & Khanesar, M. A. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2345353 · Cited by 95 · see citing works
Deep learning with 3D-second order difference plot on respiratory sounds
Altan, G., Kutlu, Y., Pekmezci, A. Ö., & Nural, S. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.05.014 · Cited by 88 · see citing works
Amin, U., Biswas, S., Elias, S. M., Razzaque, S., Haque, T., Malo, R., & Seraj, Z. I. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00014 · Cited by 82 · see citing works
Randomized algorithms for nonlinear system identification with deep learning modification
de la Rosa, E., & Yu, W. (2015) · Information Sciences · doi:10.1016/j.ins.2015.09.048 · Cited by 79 · see citing works
Sơn, N. N., Kien, C. V., & Ánh, H. P. H. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.06.012 · Cited by 76 · see citing works
A deep belief network with PLSR for nonlinear system modeling
Qiao, J., Wang, G., Li, W., et al. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.10.006 · Cited by 75 · see citing works
Kumar, R., Srivastava, S., Gupta, J. R. P., & Mohindru, A. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.073 · Cited by 70 · see citing works
Medjber, A., Guessoum, A., Belmili, H., et al. (2016) · Energy · doi:10.1016/j.energy.2016.03.026 · Cited by 68 · see citing works
A self-organizing deep belief network for nonlinear system modeling
Qiao, J., Wang, G., Li, X., & Li, W. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.01.019 · Cited by 60 · see citing works
Multi-criteria active deep learning for image classification
Yuan, J., Hou, X., Xiao, Y., et al. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.013 · Cited by 57 · see citing works
Neural network control of seat vibrations of a non-linear full vehicle model using PMSM
Güçlü, R., & Gülez, K. (2007) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2007.08.013 · Cited by 55 · see citing works
Image-based process monitoring using deep learning framework
Lyu, Y., Chen, J., & Song, Z. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.03.008 · Cited by 48 · see citing works
Multilayer feed-forward artificial neural networks for class modeling
Marini, F., Magrì, A. L., & Bucci, R. (2006) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.07.004 · Cited by 41 · see citing works
Online learning based on adaptive learning rate for a class of recurrent fuzzy neural network
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04372-w · Cited by 39 · see citing works
Self-citationA Novel Structure of Actor-Critic Learning Based on an Interval Type-2 TSK Fuzzy Neural Network
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2949554 · Cited by 39 · see citing works
Self-citationNeural network control of nonlinear dynamic systems using hybrid algorithm
Nasr, M. B., & Chtourou, M. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.07.023 · Cited by 34 · see citing works
Embedded two level direct adaptive fuzzy controller for DC motor speed control
Zaki, A. M., El-Bardini, M. A., Soliman, F. A. S., et al. (2015) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2015.10.003 · Cited by 34 · see citing works
Self-citationDeep learning for part identification based on inherent features
Krüger, J., Lehr, J., Schlüter, M., et al. (2019) · CIRP Annals · doi:10.1016/j.cirp.2019.04.095 · Cited by 33 · see citing works
Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2018) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2018.06.031 · Cited by 32 · see citing works
Self-citationAn online trained fuzzy neural network controller to improve stability of power systems
Farahani, M. A., & Ganjefar, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.047 · Cited by 22 · see citing works
Embedded Adaptive Fuzzy Controller Based on Reinforcement Learning for DC Motor with Flexible Shaft
Aziz Khater, A., El-Bardini, M., & El-Rabaie, N. M. (2015) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-015-1752-4 · Cited by 20 · see citing works
Self-citationThe nature of unsupervised learning in deep neural networks: A new understanding and novel approach
Golovko, V. A., Kroshchanka, A., & Treadwell, D. (2016) · Optical Memory and Neural Networks · doi:10.3103/s1060992x16030073 · Cited by 20 · see citing works
Neural network based controllers for the oil well drilling process
Ribeiro, V. D. J. D. S., Oliveira, G. F. D. M., Cristian, M., et al. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.01.078 · Cited by 16 · see citing works
A new technique for restricted Boltzmann machine learning
Golovko, V. A., Kroshchanka, A., Turchenko, V., et al. (2015) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2015.7340725 · Cited by 15 · see citing works
Speed control of DC motor based on an adaptive feed forward neural IMC controller
Zaineb, B. M., Abid, A., Mouna, B. H., et al. (2017) · International Conference on Green Energy Conversion Systems (GECS) · doi:10.1109/gecs.2017.8066278 · Cited by 12 · see citing works
Performance Improvement of Fuzzy Logic Controller Using Neural Network
Rajan, S., & Sahadev, S. (2016) · Procedia Technology · doi:10.1016/j.protcy.2016.05.197 · Cited by 10 · see citing works
Direct adaptive inverse control of nonlinear plants using neural networks
Shafiq, M. A. (2016) · Future Technologies Conference · doi:10.1109/ftc.2016.7821699 · Cited by 10 · see citing works
Lyapunov stability criterion based neural inverse tracking for unknown dynamic plants
Aftab, M. S., Shafiq, M., & Yousef, H. (2015) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2015.7125118 · Cited by 7 · see citing works
Neural Based Controller for Smart Detection of Crack in Cracked Cantilever Beam
Sutar, M. K., Pattnaik, S., & Rana, J. (2015) · Materials Today Proceedings · doi:10.1016/j.matpr.2015.07.225 · Cited by 6 · see citing works
ANN based intelligent controller for inverted pendulum system
Upadhyay, D., Tarun, N., & Nayak, T. (2013) · Students Conference on Engineering and Systems · doi:10.1109/sces.2013.6547526 · Cited by 5 · see citing works
Neural network delayed control of an idealized offshore steel jacket platform
Cai, Z., Zhang, B., & Yu, X.-H. (2017) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2017.8113956 · Cited by 2 · see citing works
Implementation of a neural network controller and estimator using a digital signal processing chip
Ahn, I., & Lan, J.-H. (1995) · Mathematical and Computer Modelling · doi:10.1016/0895-7177(94)00200-8 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system
Khater, A. A., Fekry, M., El-Bardini, M., & El-Nagar, A. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10741-x · Cited by 17 · see citing works
“(20) and (21) are both satisfied, then the system is considered to be asymptotically stable [27, 28].”
Self-citation
- Authors
- Zaki, A. M., El-Nagar, A. M., El-Bardini, M., & Soliman, F. A. S.
- Affiliations
- Menoufia University (EG)
- Nuclear Materials Authority (EG)
- Corresponding author
- El-Nagar, Ahmad M.
- ORCID
- El-Nagar, Ahmad M.
- Venue
- Neural Computing and Applications
- Published
- 2020-06-13
- Volume
- 33
- Issue
- 5
- Pages
- 1515-1531
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3034862588
- Cited by
- 29
- References
- 53
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 8 · 2022: 6 · 2021: 4 · 2020: 1
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computing and Applications (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- nonlinear systems, Lyapunov stability, feedforward neural networks, restricted Boltzmann machine, unsupervised pre-training, system uncertainty, external disturbances, parameter variation, electromechanical systems, Arduino
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review