Search Results - (( variable activation function algorithm ) OR ( its application force algorithm ))

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  1. 1
    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 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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  2. 2
    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations

    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

    Published 2022
    “…The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. …”
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  3. 3
    Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement

    Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement by Abdul Rahman, Nor Farahaida

    Published 2016
    “…Utilization of soft-computing algorithms in the operation of Shunt Active Power Filters (SAPFs) becomes a latest trend. …”
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  4. 4
    Real time De-mixing system based on LMS adaptive algorithm for blind two source signals separation

    Real time De-mixing system based on LMS adaptive algorithm for blind two source signals separation by Mehrkanoon, S., Moghavvemi, M., Fariborzi, H.

    Published 2007
    “…The time variant mixing matrix based on random vector with time variable elements are made. Several simulations obtain optimum results of implemented algorithm. …”
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  5. 5
    A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru

    A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru by Wang, Yanru

    Published 2018
    “…In the study, the effort to find better neural network algorithms in the application of impact force indetification continues. …”
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  6. 6
    MotionSure: a cloud-based algorithm for detection of injected object in data in motion

    MotionSure: a cloud-based algorithm for detection of injected object in data in motion by Islam, Thouhedul, Olanrewaju, Rashidah Funke, Khalifa, Othman Omran

    Published 2017
    “…Mostly, the Man In The Middle (MITM) attack happens in this stage by hijacking active session variables, manipulating files and objects. …”
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  7. 7
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A. H., M. S. A., Radzak, Musa, Mailah, Nukman, Yusoff, Abd Razak, Bushroa, Mohd Sayuti, Ab. Karim, Ameen, Wadea, Jabbar, Waheb A., Alsewari, Abdulrahman A., Rassem, Taha H., Abdullah Nasser, Mohammed Abdullah, Abdulghafor, Rawad

    Published 2021
    “…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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  8. 8
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A. H., Radzak, Muhammad S. A., Mailah, Musa, Yusoff, Nukman, Abd Razak, Bushroa, Ab.Karim, Mohd Sayuti, Ameen, Wadea, Jabbar, Waheb A., Alsewari, Abdulrahman A., Rassem, Taha H., Nasser, Abdullah B., Abdulghafor, Rawad

    Published 2021
    “…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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  9. 9
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A.H., Radzak, Muhammad S.A., Mailah, Musa, Yusoff, Nukman, Abd Razak, Bushroa, Ab. Karim, Mohd Sayuti, Ameen, Wadea, A. Jabbar, Waheb, Alsewari, AbdulRahman A., Rassem, Taha H., Nasser, Abdullah B., Abdulmolla Abdulghafor, Rawad Abdulkhaleq

    Published 2021
    “…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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  10. 10
    An Intelligent Active Force Control Algorithm to Control an Upper Extremity Exoskeleton for Motor Recovery

    An Intelligent Active Force Control Algorithm to Control an Upper Extremity Exoskeleton for Motor Recovery by Wan Hasbullah, Mohd Isa, Zahari, Taha, Ismail, Mohd Khairuddin, Anwar, P. P. A. Majeed, Khairul Fikri, Muhammad, Ali, Mohammed A. H., Jamaluddin, Mahmud, Zulkifli, Mohamed

    Published 2016
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  11. 11
    Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance

    Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

    Published 2019
    “…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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  12. 12
    Process Planning Optimization In Reconfigurable Manufacturing Systems

    Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

    Published 2008
    “…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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  13. 13
    Software User Interface and Algorithm Development for Signal and Noise Characterization

    Software User Interface and Algorithm Development for Signal and Noise Characterization by Phoong, Wei Siang

    Published 2015
    “…Atomic force microscopy (AFM) also known as “Scanning force microscopy (SFM)” is high resolution scanning probe. …”
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  14. 14
    Application of inerter in passenger vehicle suspension systems / Soong Ming Foong

    Application of inerter in passenger vehicle suspension systems / Soong Ming Foong by Soong, Ming Foong

    Published 2015
    “…This research aims to achieve greater application of inerter in passenger vehicle suspensions by studying several changes or modifications in employing the inerter and its concept. …”
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  15. 15
    Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support

    Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support by Abdullah S., Ismail M., Ahmed A.N., Abdullah A.M.

    Published 2023
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  16. 16
    Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system

    Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system by Al-Kayiem, H.H., Al-Naimi, F.B.I., Amat, W.N.B.W.

    Published 2014
    “…The results showed that the NN with two hidden layers performed better than one hidden layer using Levenberg-Maquardt training algorithm. Moreover, it was noticed that hyperbolic tangent function for input and output nodes performed better than other activation function types. …”
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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
    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus

    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

    Published 2016
    “…Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. …”
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  19. 19
    Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid

    Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid by Al Mamari, Adnan Saif, Toha, Siti Fauziah, Ahmad, Salmiah, Al Mamari, Ali Salim

    Published 2021
    “…Reduction of system active power loss is the goal of the function in the projected algorithm. …”
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  20. 20
    Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin

    Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin by Kamaruddin, Nur Amalina

    Published 2020
    “…The classification model was built using Naïve Bayes algorithm. The number of data used in this project is 15952 with 1 independent variable and 1 dependent variables. …”
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