Search Results - (( using adaptive learning algorithm ) OR ( simulation optimization method algorithm ))
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. …”
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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach
Published 2019“…In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method. © 2019 IEEE.…”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
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Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
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. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. …”
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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…Dynamic characterisations of one-dimensional flexible beam and two-dimensional flexible plate structures are presented and simulation algorithms characterising the behaviour of each structure is developed using finite difference methods. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
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. …”
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Box-jenkins and genetic algorithm hybrid model for electricity forecasting system
Published 2005“…This study proposed the possibility of using GA’s approach as one of the unique forecasting method. …”
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A Study on Abstract Policy for Acceleration of Reinforcement Learning
Published 2014“…Simulations show good result in terms of the learning acceleration and the adaptation of abstract policy.…”
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Conference or Workshop Item -
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). …”
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Deep learning-based item classification for retail automation
Published 2025“…The final system was deployed and tested in a simulated retail environment, where its performance was evaluated using metrics such as accuracy, precision, recall and F1-score. …”
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Final Year Project / Dissertation / Thesis -
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Intelligent traffic lights using Q-learning
Published 2022“…The Q-learning algorithm has been applied to the traffic lights system in this study. …”
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EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT
Published 2025“…Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. …”
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Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization
Published 2025“…The dataset used in this study is a battery RUL dataset retrieved from an open-source platform Kaggle, which consists of more than 15,000 rows of time series data. …”
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Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour
Published 2010“…These results show that,for all the arrays (2D and 3D) except 3D pole - dipole data, resilient propagation is the most efficient algorithm for training the DC resistivity data. In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems
Published 2011“…Over the years, many improvements and modifications of the back propagation learning algorithm have been reported. In this research, we propose a new modified back propagation learning algorithm by introducing adaptive gain together with adaptive momentum and adaptive learning rate into weight update process. …”
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Optimization of turning parameters using genetic algorithm method
Published 2008“…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
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Undergraduates Project Papers -
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…This thesis proposes and simulates the three novel optimization algorithms to handle DG allocation, different single-objective, and multi-objective OPF problems. …”
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