Search Results - (( parameter estimation sensor algorithm ) OR ( global optimization method algorithm ))
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Observer-based fault detection approach using fuzzy adaptive poles placement system with real-time implementation
Published 2021“…These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. …”
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan
Published 2013“…The algorithm will be developed in MATLAB. The objective of this project is to develop ANN model for ISFET sensor that able to estimate the main ion in mixed solution by learning the pattern of the input and output of the sensor. …”
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Thesis -
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor: article / Muhammad Nasrul Hakim Adenan and Maizatul Zolkapli
Published 2013“…The algorithm will be developed in MATLAB. The objective of this project is to develop ANN model for ISFET sensor that able to estimate the main ion in mixed solution by learning the pattern of the input and output of the sensor. …”
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Performance study of direction of arrival (DOA) estimation algorithms for linear array antenna
Published 2009“…The analysis is based on linear array antenna and the calculation of the pseudospectra function of the estimation algorithms. Matlab is used for simulating the algorithms.…”
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Proceeding Paper -
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Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…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
Published 2025“…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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Artificial intelligence techniques applied as estimator in chemical process systems - A literature survey
Published 2015“…The versatility of Artificial Intelligence (AI) in process systems is not restricted to modelling and control,only, but also as estimators to estimate the unmeasured parameters as an alternative to the conventional observers and hardware sensors. …”
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A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf.
Published 2013“…Since the aforementioned algorithm has been developed for other types of sensors, an approach using weighted mean value of parameters for SeaWiFS, MERIS, VIIRS, and OCTS sensors were employed to estimate parameter values for QuickBird image. …”
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Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Published 2008“…This work also presents the estimation of the rotor position, which works effectively with nearly zero estimation error over wide speed range, to replace the electrometrical rotor position sensor. …”
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Thesis -
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A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems
Published 2018“…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithm and Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. …”
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Global gbest guided-artificial bee colony algorithm for numerical function optimization
Published 2018“…The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. …”
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Sensor-less vector control using adaptive observer scheme for controlling the performance of the induction motor / Mazhar Hussain Abbasi
Published 2013“…Internal parameters are used, for example, feed-forward compensator of current controller and parameters of observer model in sensor less position. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…The proposed estimation approach simplifies the typical trial-and-error method. …”
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A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
Published 2020“…However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. …”
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Conference or Workshop Item -
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Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems
Published 2017“…This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm for global optimization problems. …”
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Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis
Published 2014“…The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. …”
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A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408)
Published 2016“…Several global methods have been proposed for solving discrete optimization problems. …”
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Monograph -
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…The proposed estimation approach simplifies the typical trial-and-error method. …”
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Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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