Search Results - (( developing function learning algorithm ) OR ( based application optimization algorithm ))

Refine Results
  1. 1

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…In order to address the above mentioned challenges, this study is devoted towards the development of a clusterer and a clustering ensemble learning method based on incremental genetic algorithms addressing group unlabeled samples. …”
    Get full text
    Get full text
    Thesis
  2. 2
  3. 3

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
    Get full text
    Get full text
    Get full text
    Thesis
  4. 4

    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

    Published 2024
    “…To address this issue, this study incorporated joint graph learning from the gmc algorithm into swmcan, creating a new algorithm called swmcan-jg. …”
    Get full text
    Get full text
    Thesis
  5. 5
  6. 6

    Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

    Published 2023
    “…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
    Get full text
    Get full text
    Get full text
    Thesis
  7. 7

    Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm by Zainuddin, Zarita

    Published 2001
    “…In this thesis, factors that govern the learning speed of the backpropagation algorithm are investigated and mathematically analyzed in order to develop strategies to improve the performance of this neural network learning algorithm. …”
    Get full text
    Get full text
    Thesis
  8. 8

    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
    “…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
    Get full text
    Get full text
    Thesis
  9. 9

    Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems by Salih, Dhiadeen Mohammed

    Published 2015
    “…The WNMPC is developed by a proposed algorithm named adaptive updating rule (AUR) used with gradient descent optimization method to minimize a constrained cost function over the prediction and control horizons and to offer a robust control performances. …”
    Get full text
    Get full text
    Thesis
  10. 10

    Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

    Published 2011
    “…The reason is that, SRT algorithm is sensitive to the accuracy of the learned preferences while MGT algorithm can generate Pareto-optimal offers even with an approximation of the learned preferences.…”
    Get full text
    Get full text
    Thesis
  11. 11

    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    “…The dissertation aims to develop an effectively decomposed time-series nongradient- based artificial intelligence model for forecasting a time-series regression machine learning task. …”
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. …”
    Article
  13. 13
  14. 14
  15. 15

    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

    Published 2020
    “…The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyperheuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.…”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    Quasi oppositional—Manta ray foraging optimization and its application to pid control of a pendulum system by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir, Nurul Amira, Mhd Rizal, Nor Maniha, Abd Ghani, Mohd Falfazli, Mat Jusof, Mohd Ashraf, Ahmad

    Published 2022
    “…From the study, MRFO is a relatively new developed algorithm and has low convergence rate. However, MRFO has potential to be improved in that aspect. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  17. 17

    A Stepper Motor Design Optimization Using by Wong, Chin Wei

    Published 2005
    “…There is a need to fill this void in the area of small-motor design, and develop a program using Genetic Algorithms (GAs) as an approach to achieve optimization. …”
    Get full text
    Get full text
    Monograph
  18. 18

    Chaos search in fourire amplitude sensitivity test by Koda, Masato

    Published 2012
    “…This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. …”
    Get full text
    Get full text
    Get full text
    Article
  19. 19

    Chaos Search in Fourier Amplitude Sensitivity Test by Koda, Masato

    Published 2012
    “…This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. …”
    Get full text
    Get full text
    Get full text
    Article
  20. 20