Search Results - (( wave application means algorithm ) OR ( its application learning algorithm ))*

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    Sactive Noise Control Technique Application in Air Conditioning Ducts by Decruz, Aloysius

    Published 2009
    “…An algorithm, ‘Hardware-Tuned Feedback ANC (HTFA)’, has been developed to implement the ANC technique for the noise reduction application in an air conditioning duct element, where Digital Signal Processing is used to sample noise and produce a complete anti-phase noise produced by the HTFA algorithm. …”
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    Thesis
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    Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach by Jumahat, Shaliza, Gan, Kok Beng, Misran, Norbahiah, Islam, Mohammad Tariqul, Mahri, Nurhafizah, Ja'afar, Mohd. Hasni

    Published 2020
    “…Besides insensitive to morphological variations of ECG waves, the computational method is simple and possibly implemented as the basis for future software development for portable device applications.…”
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    Article
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    A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

    Published 2006
    “…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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    Proceeding Paper
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    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA by AHMED IZZELDIN, HUZAIFA TAWFEIG

    Published 2012
    “…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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    Thesis
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    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

    Published 2020
    “…Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. …”
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    Article
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    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. …”
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    Thesis
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    Interferometric array planning using division algorithm for radio astronomy applications by Kiehbadroudinezhad, Shahideh

    Published 2017
    “…In the second scheme, a genetic algorithm is developed, in order to optimize a correlator array of antennas by using Genetic Algorithm (GA). …”
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    Thesis
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    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding by Mahmoud, Omer, Anwar, Farhat, Salami, Momoh Jimoh Emiyoka

    Published 2007
    “…The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.…”
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    Article
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    Machine learning: tasks, modern day applications and challenges by Aljuaid, Lamyaa Zaed, Koh, Tieng Wei, Sharif, Khaironi Yatim

    Published 2019
    “…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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    Article
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    Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

    Published 2018
    “…Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. …”
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    Article
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