Search Results - (( parameter estimation force algorithm ) OR ( using codification based algorithm ))

Refine Results
  1. 1

    A hybrid intelligent active force controller for robot arms using evolutionary neural networks by Hussein, S.B, Jamaluddin, H, Mailah, M, Zalzala, A.M.S

    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. …”
    Get full text
    Get full text
    Article
  2. 2

    Hybrid intelligent active force controller for robot arms using evolutionary neural networks by Hussein, S. B., Jamaluddin, H., Mailah, M., Zalzala, A. M. S.

    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. …”
    Get full text
    Get full text
    Article
  3. 3

    Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms by Mailah, Musa, Ong, Miaw Yong

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  4. 4

    Adaptive active force control of a robotic arm employing twin iterative learning algorithms / Musa Mailah and Ong Miaw Yong by Mailah, Musa, 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. …”
    Get full text
    Get full text
    Get full text
    Article
  5. 5

    An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme by Hussein, Shamsul Bahri, Jamaluddin, Hishamuddin, Mailah, Musa

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  6. 6

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    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. …”
    Get full text
    Get full text
    Thesis
  7. 7

    Enhancement of impact force determination with modal transformation method by using integration and data filtering /Khoo Shin Yee by 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. …”
    Get full text
    Get full text
    Thesis
  8. 8
  9. 9

    Reduced-rank technique for joint channel estimation in TD-SCDMA systems. by Ismail, Alyani, Sali, Aduwati, Mohd Ali, Borhanuddin, Khatun, Sabira

    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. …”
    Get full text
    Get full text
    Article
  10. 10

    Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems by Marzook, Ali Kamil, Ismail, Alyani, Mohd Ali, Borhanuddin, Sali, Aduwati, Khalaf, Mohannad H., Khatun, Sabira

    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. …”
    Get full text
    Get full text
    Article
  11. 11

    Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems by Sabira, Khatun, Ali K., Marzook, Alyani, Ismail, Aduwati, Sali, Mohannad Hamed, Khalaf, Borhan, M. Ali

    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. …”
    Get full text
    Get full text
    Article
  12. 12

    Establishment of spectral subtraction-based algorithm for experimental modal analysis under operating condition by Che Ku Eddy Nizwan, Che Ku Husin

    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. …”
    Get full text
    Get full text
    Thesis
  13. 13

    Crowd behavior monitoring using self-adaptive social force model by Wan Nur Azhani, W. Samsudin, Kamarul Hawari, Ghazali

    Published 2019
    “…The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  14. 14

    Non-parametric induction motor rotor flux estimator based on feed-forward neural network by Siti Nursyuhada, Mahsahirun, Nik Rumzi, Nik Idris, Zulkifli, Md. Yusof, Sutikno, Tole

    Published 2022
    “…This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15
  16. 16

    Adaptive impedance control for unknown non-flat environment by Zainul Azlan, Norsinnira, Yamaura, Hiroshi

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

    Published 2020
    “…Firstly, an improved EM (IEM) algorithm is presented to estimate the five parameters of the single PV-module system. …”
    Get full text
    Get full text
    Thesis
  18. 18

    Coherence Function of Wind and Waves ofMetocean Data by Mohamad Nasir, Quailid Rezza

    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. …”
    Get full text
    Get full text
    Final Year Project
  19. 19

    An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization by Majid, Mad Helmi Ab.

    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. …”
    Get full text
    Get full text
    Thesis
  20. 20

    Fraud detection in telecommunication using pattern recognition method / Mohd Izhan Mohd Yusoff by Mohd Yusoff, Mohd Izhan

    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. …”
    Get full text
    Get full text
    Get full text
    Thesis