Search Results - (( system implementation using algorithm ) OR ( parameter activation function algorithm ))
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Real time nonlinear filtered-x lms algorithm for active noise control
Published 2012“…However, unlike the other algorithms, NLFXLMS cannot be implemented in real time. …”
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2
Parametric modelling of twin rotor system using chaotic fractal search algorithm
Published 2016“…Then, the modified Fractal Search algorithms are employed to optimise the parameters for an ARX model of twin rotor system in hovering mode. …”
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3
Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…There have two most uses activation function namely tansig and logsig. …”
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4
All-pass filtered x least mean square algorithm for narrowband active noise control
Published 2018“…Here first-order all pass filters with single parameter is used to improve the convergence of the LMS algorithm. …”
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Embedded Artificial Intelligent (AI) To Navigate Cart Follower
Published 2018“…The training algorithm may also vary with different sets of parameters, number of neurons and activation function. …”
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6
Performance Analysis of Active Power Filter for Harmonic Compensation using PI-PSO
Published 2015“…The simulated system is a three phase balanced voltage system with nonlinear load .A particle swarm optimization (PSO) is implemented to optimize the gains of a proportional-integral (PI) algorithm to control the SAPF. …”
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Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system
Published 2024“…To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). …”
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Development of compound clustering techniques using hybrid soft-computing algorithms
Published 2006“…The hierarchical fuzzy clustering algorithm developed in this work assign the overlapping structures (structures having more than one activity) to more than one clusters if their fuzzy membership values are significantly high for those clusters. …”
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Solving power system state estimation using orthogonal decomposition algorithm / Tey Siew Kian
Published 2009“…This optimal state estimate and corrected data base are then used by the security monitoring and operation and control functions of the center.Most state estimation programs in practical use are formulated as overdetermined systems (Pozrikidis, 2008) of nonlinear equations and solved as weighted least square problems (refer to section 2.1.1).This research involves finding the least squares solution of the power system state estimation problem, HTR-1HDx = HTR-1 [z - f (x)] (refer to section 2.3.1) and to develop a program to implement the said algorithm. …”
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10
Modeling of Functional Electrical Stimulation (FES): Powered Knee Orthosis (PKO) assisted gait exercise in post-stroke rehabilitation / Adi Izhar Che Ani
Published 2023“…In the human gait model, three Machine Learning algorithms were used: Gaussian Process Regression, Support Vector Machine, and Decision Tree. …”
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11
Pid-aco vibration controller with magnetorheological damper for wind turbine tower / Mahmudur Rahman
Published 2019“…It is observed that the real implementation semi-active PID-ACO control system with MR damper reduces up to 73% and 40% of vibration displacement under harmonics excitation at tower 1st mode and random disturbances respectively. …”
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12
Determining penetration limit of central distributed generation topology in radial distribution networks
Published 2021“…The beta probability density functions were used to model the photovoltaic generation, while the normal probability density functions were used to model the load demand. …”
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13
PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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14
Monitoring water quality in Pusu river using Internet of Things (IoT) and Machine Learning (ML)
Published 2024“…Pollution from sediments and human activities carries harmful contaminants, reduces visibility, disrupts aquatic life, and impairs ecosystem function. …”
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Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations
Published 2022“…The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. …”
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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo...
Published 2025“…Here, in this research by comprising three of the most effective variational techniques now, a Modified Differential Evolutionary Optimization Algorithm (MDEOA) method is suggested to handle the challenge of adjusting the PID controller parameters for the Intelligent Active Vibration Control (IAVC) of a Combined Single Link Robotics Flexible Manipulator (CSLRFM) in order to reduce the undesired effects of vibration. …”
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The effect of adaptive parameters on the performance of back propagation
Published 2012“…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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19
A harmony search-based learning algorithm for epileptic seizure prediction
Published 2016“…The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. …”
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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