Search Results - (( parameter estimation force algorithm ) OR ( using codification based algorithm ))
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A hybrid intelligent active force controller for robot arms using evolutionary neural networks
Published 2000“…In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. …”
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Hybrid intelligent active force controller for robot arms using evolutionary neural networks
Published 2000“…In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. …”
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Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms
Published 2001“…The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. Two main iterative learning algorithms are utilized in the study – the first is used to automatically tune the controller gains while the second to estimate the inertia matrix of the manipulator. …”
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Adaptive active force control of a robotic arm employing twin iterative learning algorithms / Musa Mailah and Ong Miaw Yong
Published 2004“…The paper highlights a novel and robust method to control a robotic arm using iterative learning technique embedded in an active force control strategy. Two iterative learning algorithms are employed in the study - the first is used to tune automatically the controller gains while the second to estimate the inertia matrix of the robotic arm. …”
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5
An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme
Published 1999“…This paper presents a new intelligent controller algorithm comprising an on-line multi-layer artificial neural network (ANN) training scheme to estimate the inertia matrix of the robot arm to enhance the performance of the active force control (AFC) scheme. …”
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Active force control with iterative learning control algorithm for a vehicle suspension
Published 2013“…The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. …”
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7
Enhancement of impact force determination with modal transformation method by using integration and data filtering /Khoo Shin Yee
Published 2013“…The transformation from high condition number of synthesised FRF matrix to a well-conditioned case is demonstrated by adding additional information of force location. The low quality of fitting a modal model by using modal parameters obtained from the polynomial curve fitting algorithm is highlighted. …”
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Reduced-rank technique for joint channel estimation in TD-SCDMA systems.
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems
Published 2012“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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Establishment of spectral subtraction-based algorithm for experimental modal analysis under operating condition
Published 2022“…Under the operating condition, the presence of unmeasurable forces causes an error in the transfer function calculation due to incomplete information of input forces. …”
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13
Crowd behavior monitoring using self-adaptive social force model
Published 2019“…The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. …”
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Non-parametric induction motor rotor flux estimator based on feed-forward neural network
Published 2022“…This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. …”
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Adaptive impedance control for unknown non-flat environment
Published 2013“…The force error feedback is utilized in the estimation and the accurate knowledge of the environment parameters are not required by the algorithm. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Firstly, an improved EM (IEM) algorithm is presented to estimate the five parameters of the single PV-module system. …”
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Coherence Function of Wind and Waves ofMetocean Data
Published 2011“…From here we are able to determine the correlation of these environmental forces to estimate the significance frequency where these forces acts in linear functions in the system, thus identify the response of the platform structural members as we assume the inputs to be stationary. …”
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An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization
Published 2019“…In order to optimize search space exploration and to maintain inter-robot communication connectivity at swarm level, a dispersion algorithm based on attraction and repulsion force is proposed. …”
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Fraud detection in telecommunication using pattern recognition method / Mohd Izhan Mohd Yusoff
Published 2014“…Due to the complexity of GMM, we use Expectation Maximization (or EM) algorithm by Dempster et al. (1977) to obtain the maximum likelihood estimates of the GMM parameters. …”
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