Search Results - (( normal distribution a algorithm ) OR ( parameters variation step algorithm ))
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1
Backstepping Integral Super Twisting Sliding Mode Control Algorithm For Autonomous Underwater Glider
Published 2019“…The BISTSMC was benchmarked with integral SMC (ISMC), super twisting SMC (STSMC), integral STSMC (ISTSMC), back-stepping ISMC and back-stepping STSMC. The simulation results have shown that the proposed controller provides the smallest chattering about more than 100 times smaller than ISTSMC, more than 10000 times smaller than backstepping ISMC and backstepping STSMC in nominal, disturbance and parameter variation cases respectively. …”
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2
Development of controller for an underactuated autonomous underwater vehicle (AUV)
Published 2019“…The BSTSMC was benchmarked with super twisting SMC (STSMC) and back-stepping SMC. The simulation results have shown that the proposed controller provides the smallest chattering about more than 1000 times smaller than STSMC, more than 100 times smaller than back-stepping SMC in nominal, disturbance and parameter variation cases respectively. …”
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Research Report -
3
Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system
Published 2010“…These algorithms are designed to achieve the robustness over a wide range of system parameters change and disturbances. …”
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A comparative evaluation of PID-based optimisation controller algorithms for DC motor
Published 2023Article -
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Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms
Published 2022“…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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Proceedings -
7
Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control
Published 2014“…Furthermore, the proposed algorithm is robust enough to operate under different operating conditions and system parameter variations.…”
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8
Efficiency improvement of a standalone photovoltaic system using fuzzy-based maximum power point tracking algorithm
Published 2016“…This thesis proposes a FLC controller that calculates the E and the previous duty ratio variations (ΔDn-1) as input parameters to adaptively modify the output duty ratio (Δd). …”
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9
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…Statistical techniques such as Anisotropic Diffusion Filter (ADF), local contrast enhancement, and haze reduction in the CIELAB colour space, are incorporated in the proposed algorithms for the pre-processing step. Hair removal is implemented through black-hat morphological processing and total variation based inpainting. …”
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10
A modified sine cosine algorithm for improving wind plant energy production
Published 2019“…This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. …”
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Cellular Harmony Search for Optimization Problems
Published 2013“…Structured population in evolutionary algorithms (EAs) is an important research track where an individual only interacts with its neighboring individuals in the breeding step. …”
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Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. The percentage level 5% under original bandwidth used is developed on policing and shaping algorithms to control bandwidth used. …”
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Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Published 2008“…However, these controllers are very sensitive to step change of command speed, parameter variations and load disturbance. …”
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14
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…The focus of this analysis is on original observations that performed a detecting outlier and power transformation to transform not normally distributed data to near normality. …”
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15
Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm
Published 2022“…The simulations of step response analysis, Bode plot analysis, trajectory tracking analysis, disturbance rejection analysis, and parameter variation analysis are conducted to evaluate the effectiveness of the proposed MSFA-FOPID controller of AVR system. …”
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Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method
Published 2016“…Then, design of experiments is used to examine the sensitivity of the parameters of each solving algorithm using Taguchi method. …”
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17
Design of QFT-based self-tuning deadbeat controller
Published 2013“…This paper presents a design method of self-tuning Quantitative Feedback Theory (QFT) by using improved deadbeat control algorithm. QFT is a technique to achieve robust control with pre-defined specifications whereas deadbeat is an algorithm that could bring the output to steady state with minimum step size. …”
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Design of QFT-based self-tuning deadbeat controller
Published 2013“…This paper presents a design method of self-tuning Quantitative Feedback Theory (QFT) by using improved deadbeat control algorithm. QFT is a technique to achieve robust control with pre-defined specifications whereas deadbeat is an algorithm that could bring the output to steady state with minimum step size. …”
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19
Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023“…This work is focused on the implementation of evolutionary based computer algorithms, genetic algorithms (GAs), on microcontrollers. …”
Conference paper -
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A study on advanced statistical analysis for network anomaly detection
Published 2005“…The focus of this analysis is on original observations that performed a detecting outlier and power transformation to transform not normally distributed data to near normality. …”
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