Search Results - (( parameter estimation using algorithm ) OR ( parameter adaptation clustering algorithm ))

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  1. 1
    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption

    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

    Published 2023
    “…The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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  2. 2
    Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

    Optimization of ANFIS with GA and PSO estimating α ratio in driven piles by Moayedi, Hossein, Raftari, Mehdi, Sharifi, Abolhasan, Wan Jusoh, Wan Amizah, A. Rashid, Ahmad Safuan

    Published 2020
    “…The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. …”
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  3. 3
    Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems

    Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems by Ravari, Arastoo Rostami

    Published 2005
    “…Estimated parameters from recent measurements ([PMFOO]) are compared with estimated parameters from model generated waveforms as well as theoretical distribution of received signal's angular spread. …”
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  4. 4
    Trade-off between energy efficiency and collisions for MAC protocols of wireless sensor network

    Trade-off between energy efficiency and collisions for MAC protocols of wireless sensor network by Jahan, Mohammad Saukat

    Published 2015
    “…Secondly, this research introduces a geographical and power based clustering algorithm (GPCA) for WSNs. Trade-off between energy efficiency and collisions in these approaches can be obtained by cluster formation, cluster-head election, data collecting at the cluster-head nodes to reduce data redundancy and thus, save energy. …”
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  5. 5
    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

    Published 2019
    “…Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. …”
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  6. 6
    Time series modeling of water level at Sulaiman Station, Klang River, Malaysia

    Time series modeling of water level at Sulaiman Station, Klang River, Malaysia by Galavi, Hadi

    Published 2010
    “…The estimation of parameters of the model is accomplished using the hybrid learning algorithm consisting of standard neural network backpropagation algorithm and least squares method. …”
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  7. 7
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. …”
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  8. 8
    Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

    Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping by Kamal Z., Zamli, Kader, Md. Abdul, Azad, Saiful, Ahmed, Bestoun S.

    Published 2021
    “…Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. …”
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  9. 9
    Cluster head selection algorithm using fuzzy logic in multi-tier Wireless Sensor Network for energy efficiency / Wan Isni Sofiah Wan Din

    Cluster head selection algorithm using fuzzy logic in multi-tier Wireless Sensor Network for energy efficiency / Wan Isni Sofiah Wan Din by Wan Din, Wan Isni Sofiah

    Published 2016
    “…However, there is still a lack of effective techniques to determine and select the cluster head. Currently, the selection of cluster head is based on residual energy and several parameters. …”
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  10. 10
    Energy Balancing Through Cluster Head Selection Using K-Theorem in Homogeneous Wireless Sensor Networks

    Energy Balancing Through Cluster Head Selection Using K-Theorem in Homogeneous Wireless Sensor Networks by Muhammad , Imran, Asfandyar, khan, Azween, Abdullah

    “…This CN take up the responsibility of transmitting data to the base station over longer distances from cluster heads. We have proposed a cluster head selection algorithm based on K - theorem and other parameters i.e. residual energy, distance to coordinator node, reliability and degree of mobility. …”
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  11. 11
    Energy Balancing Through Cluster Head Selection Using K-Theorem in Homogeneous Wireless Sensor Networks

    Energy Balancing Through Cluster Head Selection Using K-Theorem in Homogeneous Wireless Sensor Networks by Muhammad , Imran, Asfandyar, khan, Azween, Abdullah

    Published 2008
    “…This CN take up the responsibility of transmitting data to the base station over longer distances from cluster heads. We have proposed a cluster head selection algorithm based on K - theorem and other parameters i.e. residual energy, distance to coordinator node, reliability and degree of mobility. …”
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  12. 12
    Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization

    Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization by Hussain Mohammad Yousef Abu Dalbouh

    Published 2024
    thesis::doctoral thesis
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  13. 13
    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed

    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

    Published 2012
    “…In conventional hard clustering approach, the number of clusters was determined by hierarchical clustering and two-step cluster analysis; then the sites were allocated to the appropriate cluster by k-means clustering method. …”
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  14. 14
    Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure

    Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure by Loo, C.K., Rajeswari, M., Rao, M.V.C.

    Published 2004
    “…However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. …”
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  15. 15
    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things

    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

    Published 2022
    “…Hence, the algorithm must overcome the problem of dynamic updates in the internal parameters or counter the concept drift. …”
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  16. 16
    Hyper-heuristic framework for sequential semi-supervised classification based on core clustering

    Hyper-heuristic framework for sequential semi-supervised classification based on core clustering by Adnan, Ahmed, Muhammed, Abdullah, Abd Ghani, Abdul Azim, Abdullah, Azizol, Huyop @ Ayop, Fahrul Hakim

    Published 2020
    “…Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. …”
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  17. 17
    Estimation in spot welding parameters using genetic algorithm

    Estimation in spot welding parameters using genetic algorithm by Lukman, Hafizi

    Published 2007
    “…By using Genetic algorithm (GA) the spot welding parameters can be estimated.…”
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  18. 18
    A framework of modified adaptive neuro-fuzzy inference engine

    A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

    Published 2012
    “…The developed TSK type fuzzy inference engine is called modified adaptive fuzzy inference engine (MAFIE) and its parameters were then adjusted by the hybrid learning algorithm using adaptive neural network architecture towards improved performance which is called MANFIE. …”
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  19. 19
    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse)

    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse) by Jia, Xing Yeoh, Chuii, Khim Chong, Mohd Saberi, Mohamad, Yee, Wen Choon, Lian, En Chai, Safaai, Deris, Zuwairie, Ibrahim

    Published 2015
    “…The results from this experiment show estimated optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.…”
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  20. 20
    Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control

    Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control by Al-Himyari, Bayadir Abbas, Yasin, Azman, Gitano, Horizon

    Published 2014
    “…One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes.To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here.Air- fuel ratio represents complex, nonlinear and stochastic behavior.To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air- fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time.This paper describes a fuzzy clustering method to initialize the ANFIS.…”
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