Search Results - (( variable activation function algorithm ) OR ( _ implementation using algorithm ))
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DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS
Published 2011“…The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. …”
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2
Optimized fuzzy logic sliding mode control with proportional-integral-derivative for an electrohydraulic actuator system
Published 2023“…A particle swarm optimization (PSO) algorithm was implemented as the FLSMC design involves a complicated procedure and mathematical formulations to obtain the optimal value of the designed control variables. …”
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3
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The extraction network is composed of rough neurons that accounts for the upper and lower approximations and embeds a membership function to replace ordinary activation functions. …”
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4
Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system
Published 2024“…The suggested approach has additional advantages for the optimization of functions with three variables, including simplicity in implementation, quick convergence traits, and superior computational capabilities. …”
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Assessment of proposed lateral resistance system used with framed structures
Published 2016“…The specific finite element algorithms are developed and implemented in the finite element program code for the nonlinear analysis of RC framed buildings. …”
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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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7
Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement
Published 2016“…Utilization of soft-computing algorithms in the operation of Shunt Active Power Filters (SAPFs) becomes a latest trend. …”
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8
Optical soliton perturbation with quadratic-cubic nonlinearity / Mir Asma
Published 2020“…Semi-inverse variational principle (SVP) is used to secure bright solitons that are expressed as Gauss’ hypergeometric functions. …”
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9
Real time De-mixing system based on LMS adaptive algorithm for blind two source signals separation
Published 2007“…The time variant mixing matrix based on random vector with time variable elements are made. Several simulations obtain optimum results of implemented algorithm. …”
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10
MotionSure: a cloud-based algorithm for detection of injected object in data in motion
Published 2017“…Mostly, the Man In The Middle (MITM) attack happens in this stage by hijacking active session variables, manipulating files and objects. …”
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A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments
Published 2021“…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments
Published 2021“…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments
Published 2021“…The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. …”
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14
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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15
Process Planning Optimization In Reconfigurable Manufacturing Systems
Published 2008“…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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Analysis of boiler operational variables prior to tube leakage fault by artificial intelligent system
Published 2014“…The results showed that the NN with two hidden layers performed better than one hidden layer using Levenberg-Maquardt training algorithm. Moreover, it was noticed that hyperbolic tangent function for input and output nodes performed better than other activation function types. …”
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Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
Published 2016“…Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. …”
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Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid
Published 2021“…Reduction of system active power loss is the goal of the function in the projected algorithm. …”
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Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin
Published 2020“…The classification model was built using Naïve Bayes algorithm. The number of data used in this project is 15952 with 1 independent variable and 1 dependent variables. …”
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