Search Results - (( parameter optimization swarm algorithm ) OR ( parameter optimization sensor algorithm ))*

Modelling of multi-robot system for search and rescue by Poy, Yi Ler

“…In this project, this sensor-based algorithm is known as the Obstacle Avoidance Algorithm. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah

“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom

“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa'

“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa’

“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). …”
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Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning by Yan, Suqing, Luo, Baihui, Sun, Xiyan, Xiao, Jianming, Ji, Yuanfa, Kamarul Hawari, Ghazali

“…To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. …”
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State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Blaabjerg F.

Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

“…By using clustering method K-Means, we have found the best center point position to calculate Kernel matrix. at last, we have employed Quantum-behaved Particle Swarm Optimization (QPSO) to get the optimal kernel parameter in the proposed model. …”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines by Bala, A., Ismail, I., Ibrahim, R., Sait, S.M., Oliva, D.

“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique by Mohd Mawardi, Saari, Mohd Herwan, Sulaiman, Kiwa, Toshihiko

“…In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. …”
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PID-PSO DC motor position controller design for ankle rehabilitation system by Azizi, Muhammad Azizul Raziq

“…The transfer function is a model in Matlab software to validate the performance of the control system through simulation compared with real-time experiments. Next, the control algorithms are proposed to design and implement the Proportional-Integral-Derivative (PID) with Particle Swarm Optimization (PSO) controller technique for optimal Proportional (Kp), Integral (Ki) and Derivative (Kd) gains. …”
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Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model by Mohammed Adam, Kunna Azrag

“…In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. …”
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OPTIMIZATION OF PID CONTROLLER PARAMETERS USING ARTIFICIAL FISH SWARM ALGORITHM by SOOMRO, WAFA ALI SOOMRO

“…This Final Year Project is preceded on the topic named “The Optimization of PID Control Parameters Using Artificial Fish Swarm Algorithm”. …”
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Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation by Premkumar M., Ravichandran S., Hashim T.J.T., Sin T.C., Abbassi R.

Subjects:

Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System by Nasroddin, Saidatul Nizan, Mohd Mokji, Musa, Tan, Kok, Zainal Abidin, Amar Faiz, Amirulah, Rahman, Nordin, Nur Anis, Hasim, Saipol Hadi, Zakaria, Hamzah, Hassan, Jefery, Jaafar, Hazriq Izzuan, Khairuddin, Osman

“…This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. …”
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

“…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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Effect of parameters variation on the performance of particle swarm optimization algorithm for tag coverage problem of radio frequency identification network by Nawawi, Azli, Hasnan, Khalid, Ngali, Zamani, Sidek, Noor Azizah

“…In this paper, PSO (Particle Swarm Optimization) algorithm is used to optimize the tag coverage problem. …”
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Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram M., Othman F.B., Yaseen Z.M., Afan H.A., Allawi M.F., Malek M.B.A., Ahmed A.N., Shahid S., Singh V.P., El-Shafie A.

“…Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States…”

Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis by Asrul, Adam

“…In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. …”
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