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Hopfield Neural Networks for Parametric Identification of Dynamical Systems
2005
Neural Processing Letters
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function also varies with time. ...
In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation, in the context of system identification. ...
The careful reading and useful suggestions of the reviewers are gratefully acknowledged. ...
doi:10.1007/s11063-004-3424-3
fatcat:qhosfuhohvespd26nvdfrbf6la
Multi-Robot Energy-Efficient Coverage Control with Hopfield Networks
2020
Studies in Informatics and Control
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The control problem of the multi-robots with different actuation capabilities has caught the attention of the robotics researchers over the last years. ...
The algorithm proposed in the paper not only makes use of the energy-efficient coverage optimal control scheme but also utilizes Hopfield Neural Networks (HNN) in order to perform collaboration among the ...
In the paper (Atencia, Joya & Sandoval, 2004) , an online identification method for non-linear systems with Hopfield networks is proposed. ...
doi:10.24846/v29i2y202004
fatcat:7w7h6mb24zckpl3xecmknyzho4
Neural networks: Algorithms and applications
2008
Neurocomputing
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Wang, Jian, and Guo discuss the existence and uniqueness and the global exponential stability of the equilibrium point for Cohen-Grossberg type BAM neural networks with time-varying delays and continuously ...
Xu, Wang, and Liao analyze the stability of high-order Hopfield type neural networks with uncertainties which are assumed to be bounded. ...
doi:10.1016/j.neucom.2007.09.001
fatcat:bpuxqwm74vfm3m33j4wbwetnmu
2009 Index IEEE Transactions on Automatic Control Vol. 54
2009
IEEE Transactions on Automatic Control
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., +, TAC Sept.
2009 2114-2125
Hopfield neural nets
Performance Analysis of Gradient Neural Network Exploited for Online
Time-Varying Matrix Inversion. ...
., +, TAC May 2009 1019-1024 Stability of Networked Control Systems With Uncertain Time-Varying Delays. Cloosterman, M. B. ...
State Convergence of Passive Nonlinear Systems With an L Input. ...
doi:10.1109/tac.2009.2037798
fatcat:4ilhkzss6jc63ersjzi47hiwgu
Neurodynamics in the Sensorimotor Loop: Representing Behavior Relevant External Situations
2017
Frontiers in Neurorobotics
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This is carefully done by addressing the problem in three steps, using the time-discrete dynamics of standard neural networks and a fiber space representation for better clearness. ...
a class of sensor inputs all generating the "same type" of dynamic behavior, and a dynamical form comprises the corresponding class of parametrized dynamical systems. ...
* ∈ Q a set of dynamical systems (A, f ρ ) which are parametrically stable with respect to ρ * ∈ Q. ...
doi:10.3389/fnbot.2017.00005
pmid:28217092
pmcid:PMC5289985
fatcat:2hcax5fwojgqfgadgb4uqrz6vu
A Novel Recurrent Adaptive Backstepping Optimal Control Strategy for a Single Inverted Pendulum System
[article]
2021
arXiv
pre-print
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Here, first of all, the backstepping control laws are investigated based on the nonlinear dynamic model of the system. ...
At last, the stability analysis of the system is studied using Lyapunov function. ...
This problem is similar to the concept of training of the Hopfield network. ...
arXiv:2110.09846v1
fatcat:lvulahp355cx7a23gjimodepey
Neural Networks and Their Application to Power Engineering
[chapter]
1991
Control and Dynamic Systems
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This is a key idea that could be applied to training NN' s for problems with time varying power system topologies. ...
LOAD FORECASTING Forecasting electrical load in a power system with lead-times varying from hours to days, has obvious economic as well as other advantages. ...
doi:10.1016/b978-0-12-012741-2.50012-9
fatcat:3qhnsfbe6bhxnihk6tmelo5jbi
Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey
2022
Intelligence & Robotics
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based on the experience they have with the system while training or possibly enhance it in real-time as well. ...
In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and for control of dynamical systems in particular. ...
problem for discrete-time systems with control constraints NNs: Neural Networks. ...
doi:10.20517/ir.2021.19
fatcat:xwp7dc3j6rdrraumuc5xigzici
IEEE Robotics & Automation Society
2012
IEEE robotics & automation magazine
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INESC-ID
The problem of time-varying parameter identification is considered
on a class of nonlinear hybrid systems. ...
At each time instant, this network produces an estimate of the beam parameters and this estimate is the same for all beam points. In turn, the second method combines several Hopfield neural networks. ...
doi:10.1109/mra.2012.2230568
fatcat:33actbknxrel3jnag2kx7cncem
IEEE Robotics & Automation Society
2011
IEEE robotics & automation magazine
Preserved Fulltext
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INESC-ID
The problem of time-varying parameter identification is considered
on a class of nonlinear hybrid systems. ...
At each time instant, this network produces an estimate of the beam parameters and this estimate is the same for all beam points. In turn, the second method combines several Hopfield neural networks. ...
doi:10.1109/mra.2011.941112
fatcat:owvu2behc5hulpcae2dp5myigm
[IEEE Robotics & Automation Society]
2012
IEEE robotics & automation magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
INESC-ID
The problem of time-varying parameter identification is considered
on a class of nonlinear hybrid systems. ...
At each time instant, this network produces an estimate of the beam parameters and this estimate is the same for all beam points. In turn, the second method combines several Hopfield neural networks. ...
doi:10.1109/mra.2012.2229854
fatcat:rjrxtwk4jbcgjpvjdad6mougsq
IEEE Robotics & Automation Society
2011
IEEE robotics & automation magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
INESC-ID
The problem of time-varying parameter identification is considered
on a class of nonlinear hybrid systems. ...
At each time instant, this network produces an estimate of the beam parameters and this estimate is the same for all beam points. In turn, the second method combines several Hopfield neural networks. ...
doi:10.1109/mra.2011.943480
fatcat:d2wvloyv6jcbzp2yathd52mx2u
Neuronal Sequence Models for Bayesian Online Inference
[article]
2020
arXiv
pre-print
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Importantly, it is promising to translate the key idea of probabilistic inference on sequences to machine learning, in order to address challenges in the real-time recognition of speech and human motion ...
Combining experimental findings with computational concepts like the Bayesian brain hypothesis and predictive coding leads to the interesting possibility that predictive and inferential processes in the ...
A heteroclinic network is a dynamic system with semi-stable states (saddle points) which are connected by phase-space trajectories. ...
arXiv:2004.00930v1
fatcat:m6nodt3xl5adrns3zqdgmqruem
Evolutionary Robotics and Neuroscience
[chapter]
2014
The Horizons of Evolutionary Robotics
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The underlying 'electrical' network is a discrete time step, recurrent neural network with a variable number of nodes. ...
the neural system to cope with an arbitrary robotic system. ...
doi:10.7551/mitpress/8493.003.0003
fatcat:qvhpttno25f4zpjhhjzblesr7q
Design of a robust neural network-based tracking controller for a class of electrically driven nonholonomic mechanical systems
2013
Information Sciences
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of high-degree time-varying uncertainties. ...
This class of electrically driven nonholonomic mechanical systems can be perturbed by plant uncertainties, unmodeled time-varying perturbations, and external disturbances. ...
Acknowledgements The support of this work in part by the National Science Council of the Republic of China under NSC 98-2221-E-006-212-MY3 is gratefully acknowledged. ...
doi:10.1016/j.ins.2012.07.053
fatcat:ja53bwskyfchtphta2l6cw4tay
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