A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Filters
A REAL-TIME ADAPTIVE LEARNING CONTROL BASED ON EVOLUTIONARY ALGORITHM OF THE APPLICATION TO AN ELECTRO-HYDRAULIC SERVO SYSTEM
2002
Proceedings of the JFPS International symposium on fluid power
Preserved Fulltext
The mutation equation of evolutionary strategy uses the error that is generated from the dynamic system. ...
These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics ...
This thesis, to compose a real time adaptive learning controller according to the output of a dynamic system, applies an evolutionary algorithm to a feed forward neural network that is generally used by ...
doi:10.5739/isfp.2002.573
fatcat:4ueo3dwgbrcundo3rpf7oj5a7m
Data-driven Koopman operators for model-based shared control of human–machine systems
2020
The international journal of robotics research
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 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2006.07210v1 fatcat:vnaadnmsl5ckjfj5jxbaohzql4
We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible ...
Our method assumes no a priori knowledge of the system dynamics. ...
Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the aforementioned institutions. ...
doi:10.1177/0278364920921935
fatcat:ifxyziiub5g3dj7i5ijftx3toa
Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation
2021
Machines
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 2021; you can also visit the original URL.
The file type is application/pdf.
We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. ...
Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. ...
The author acknowledges the support that he received from his past lab members and Ryojun Ikeura of Mie University, Japan. ...
doi:10.3390/machines9020028
fatcat:njkimcpirbdkra7p4n5rtxoknu
Special issue on extreme learning machine and deep learning networks
2020
Neural computing & applications (Print)
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 2020; you can also visit the original URL.
The file type is application/pdf.
It is seen that before collecting the system input and output data and learning the systems dynamics, the noise is firstly used as the system control input. ...
class of continuous-time Markov jump linear systems (MJLSs) with unknown system dynamics by using a parallel reinforcement learning (RL) algorithm. ...
doi:10.1007/s00521-020-05175-0
fatcat:4a6v6gptyzhy5ncwnmhuvhgwqq
Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network
2020
Applied Sciences
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 2020; you can also visit the original URL.
The file type is application/pdf.
The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. ...
Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from ...
We designed an algorithm that learns faster by adaptively updating the reward value in the form of a real number derived from human evaluation. ...
doi:10.3390/app10072558
fatcat:q75624a7gbfofnluuwh2nxkuna
Structured Neural Network Dynamics for Model-based Control
[article]
2018
arXiv
pre-print
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 2020; you can also visit the original URL.
The file type is application/pdf.
The network is designed to mimic the properties of linear dynamical systems which makes analysis and control simple. ...
The architecture facilitates the integration of learned system models with gradient-based model predictive control algorithms, and removes the requirement of computing potentially costly derivatives online ...
An alternative option is to learn an explicit model of the system dynamics which can be incorporated into an optimal control algorithm. ...
arXiv:1808.01184v1
fatcat:mtxzyej7qvcqfcfmdabgahxhja
Improved Learning of Dynamics Models for Control
[chapter]
2017
Springer Proceedings in Advanced Robotics
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.
In this paper we present an extension to Data As Demonstrator for handling controlled dynamics in order to improve the multiple-step prediction capabilities of the learned dynamics models. ...
Model-based reinforcement learning (MBRL) plays an important role in developing control strategies for robotic systems. ...
Experimental Evaluation We evaluate our algorithm ('DAgger +DaD') both on simulated dynamical systems 2 and real robotic platforms. ...
doi:10.1007/978-3-319-50115-4_61
dblp:conf/iser/VenkatramanCPHN16
fatcat:iqvkht4kzjggbheblm6pmwy3ui
Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools
[chapter]
2009
Lecture Notes in Computer Science
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 2011; you can also visit the original URL.
The file type is application/pdf.
We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. ...
We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. ...
A constraint of the RL algorithms used is that they learn a policy for a single target and they do not allow to change the target without learning a new policy from the beginning. ...
doi:10.1007/978-3-642-02562-4_10
fatcat:65o2sferhvfofpqvnzf6aq2ao4
Using Machine Learning Approach for Computational Substructure in Real-Time Hybrid Simulation
[article]
2020
arXiv
pre-print
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 2020; you can also visit the original URL.
The file type is application/pdf.
Two different machine learning algorithms are evaluated to provide a valid and useful metamodeling solution for analytical substructure. ...
RTHS test results using both LR and RNN models are evaluated, and the advantages and disadvantages of these models are discussed. ...
Machine learning is the science of programming computers so that they can learn from data [14] . ...
arXiv:2004.02037v1
fatcat:5hfiah4jvzctnjq632qnecgmki
Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification
2015
Mathematical Problems in Engineering
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 2019; you can also visit the original URL.
The file type is application/pdf.
Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. ...
The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. ...
is used to identify a real dynamical system, considering that its experimental data is corrupted by noise and outliers. ...
doi:10.1155/2015/678965
fatcat:fivbswqxivcwhhokytwfex3qcm
Memetic robot control evolution and adaption to reality
2016
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
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 2019; you can also visit the original URL.
The file type is application/pdf.
This paper explores the effects of learning combined with a genetic algorithm to evolve control system parameters for a four-legged robot. ...
On the direct results from evolution in simulation, Lamarckian learning showed promising results, with a significant increase in final fitness compared with the results from evolution without learning. ...
A description of the experimental setup is given in the first subsection, before the results from evolution of the robot control system parameters is presented in Subsection III-B.
A. ...
doi:10.1109/ssci.2016.7850169
dblp:conf/ssci/RuudSG16
fatcat:br73isxrrved7j65smhm4yytne
Deep Koopman Operator with Control for Nonlinear Systems
[article]
2022
arXiv
pre-print
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 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Experimental results demonstrate that our approach outperforms other existing methods, reducing the prediction error by order of magnitude and achieving superior control performance in several nonlinear ...
Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control ...
Experimental results prove that this approach is also conducive to the control of general systems with high nonlinearity on control terms. ...
arXiv:2202.08004v2
fatcat:eapumwddnzgmzk2iw7s6ne63xe
Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control
[article]
2018
arXiv
pre-print
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 2020; you can also visit the original URL.
The file type is application/pdf.
Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks. ...
all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. ...
ACKNOWLEDGMENT The authors also would like to thank anonymous editor and reviewers who gave valuable suggestion that has helped to improve the quality of the manuscript. ...
arXiv:1807.00298v1
fatcat:we2c24rbnrfkbeanpjdqihxljq
Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching
2022
Computational Intelligence and Neuroscience
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 2022; you can also visit the original URL.
The file type is application/pdf.
Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students' learning behavior, this paper evaluates students' English teaching quality from ...
The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner ...
proposed that when designing a feedback controller for modeling discrete dynamic systems, the time-varying perturbation of controller parameters may lead to the performance degradation of closed-loop systems ...
doi:10.1155/2022/1914996
pmid:35720913
pmcid:PMC9203174
fatcat:a3fqpszunzgxngk4zybj3yst5a
Decentralized Multi-tasks Distribution in Heterogeneous Robot Teams by Means of Ant Colony Optimization and Learning Automata
[chapter]
2012
Lecture Notes in Computer Science
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.
We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot's error in estimating the real number of pending tasks and also the dynamic generation of ...
The paper ends with a critical discussion of experimental results. ...
Experimental Results We have carried out a series of experiments to evaluate the system performance index by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based ...
doi:10.1007/978-3-642-28942-2_10
fatcat:hl27hpev2zg53dubwy7ujjbtaq
« Previous
Showing results 1 — 15 out of 228,499 results