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  1. 1
    A hybrid intelligent active force controller for robot arms using evolutionary neural networks

    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. …”
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  2. 2
    Hybrid intelligent active force controller for robot arms using evolutionary neural networks

    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. …”
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  3. 3
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  4. 4
    Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms

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

    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. …”
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  6. 6
    An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme

    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. …”
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  7. 7
    Active force control with iterative learning control algorithm for a vehicle suspension

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

    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. …”
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  9. 9
    Pendulum-like algorithm as a local search technique

    Pendulum-like algorithm as a local search technique by Abed I.A., Koh S.P., Sahari K.S.M., Tiong S.K., Younis H.A.-K., Abed A.A.

    Published 2023
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  10. 10
    Reduced-rank technique for joint channel estimation in TD-SCDMA systems.

    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. …”
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  11. 11
    Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems

    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. …”
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  12. 12
    Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems

    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. …”
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  13. 13
    Establishment of spectral subtraction-based algorithm for experimental modal analysis under operating condition

    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. …”
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  14. 14
    Crowd behavior monitoring using self-adaptive social force model

    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. …”
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  15. 15
    Non-parametric induction motor rotor flux estimator based on feed-forward neural network

    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. …”
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  16. 16
    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
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  17. 17
    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy

    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  18. 18
    An Educational Tool Aimed at Learning Metaheuristics

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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  19. 19
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  20. 20
    Adaptive impedance control for unknown non-flat environment

    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. …”
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