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Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach
Published 2025“…Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. …”
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Optimized electric power distribution via genetic algorithm based estimation / Otonye Ene Ojuka ... [et al.]
Published 2024“…This research shows an effective way of minimizing power loss in distribution lines using optimal capacitor placement (OCP) in conjunction with a genetic algorithm (GA). …”
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Optimizing PV integration using COA: power loss reduction and voltage stability under time-varying loads / Siti Salwa Mat Isa ... [et al.]
Published 2025“…The Coyote Optimization Algorithm (COA) is employed to determine the optimal location and size of PV systems. …”
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A miniature stub-loaded antenna optimized at VHF band for FSR sensor application / Hasrul Hisyam Harun
Published 2014“…Antenna design in this paper simulated using Computer Simulation Technique (CST) and introduced Parameter Sweep and Genetic Algorithm (GA) technique to determine the optimal lengths of antenna and stubs. …”
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Optimizing n-1 contingency rankings using a nature-inspired modified sine cosine algorithm
Published 2025“…Such evaluations enable power system operators to make informed and strategic decisions during real-time scenarios. A novel approach utilizing the Modified Sine Cosine Algorithm (MSCA), a nature-inspired metaheuristic optimization technique, is proposed to resolve (N-1) contingency rankings efficiently. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System
Published 2020“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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Training functional link neural network with ant lion optimizer
Published 2020“…Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. …”
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Functional link neural network with modified bee-firefly learning algorithm for classification task
Published 2016“…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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A modified generalized RBF model with EM-based learning algorithm for medical applications
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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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
Published 2014“…Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. …”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr...
Published 2024“…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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Dynamic training rate for backpropagation learning algorithm
Published 2013“…In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper.The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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Particle swarm optimization for neural network learning enhancement
Published 2006“…Backpropagation (BP) algorithm is widely used to solve many real world problems by using the concept of Multilayer Perceptron (MLP). …”
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Support directional shifting vector: A direction based machine learning classifier
Published 2021“…The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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