Search Results - (( weight distribution means algorithm ) OR ( parameters variation case algorithm ))
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Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering
Published 2024“…To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). …”
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Backstepping Integral Super Twisting Sliding Mode Control Algorithm For Autonomous Underwater Glider
Published 2019“…The BISTSMC was tested for external disturbance and parameter variations. The BISTSMC has been benchmarked its performances with other sliding mode control (SMC) strategies to evaluate the chattering suppression of the controllers. …”
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The effect of key parameters on the design of an optimized CAES power plant
Published 2017“…In order to obtain a more tangible realization, it is necessary to verify the results against the variation of key parameters. In this study, the sensitivity analysis is performed based on main parameters including plant loading and ambient condition and the resultant trends of each case are presented. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”
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Development of controller for an underactuated autonomous underwater vehicle (AUV)
Published 2019“…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. The steady error of the proposed controller also gives the smallest steady state error of four times smaller than STSMC and back-stepping SMC in all cases for pitching angle and 100 times smaller than STSMC and back-stepping for excess mass. …”
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A novel LTE scheduling algorithm for green technology in smart grid
Published 2015“…In terms of fairness, the proposed algorithm shows 3, 7 and 9 better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.…”
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Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method
Published 2016“…It is observed that in 67% of studied cases, inflation rate can strengthen cell load variation. …”
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Tracking moving targets in wireless sensor networks using extended diffusion strategies of distributed Kalman filter
Published 2013“…As a simulation study, we applied the algorithms in a network to track the position and speed of a projectile and compared the results with real world circumstances, using the concept of transient mean square deviations of network as a cost function. …”
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Variational Bayesian inference for exponentiated Weibull right censored survival data
Published 2023“…The results from the experiments reveal that the Variational Bayesian (VB) approach is better than the competing Metropolis-Hasting Algorithm and the reference maximum likelihood estimates.…”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…From the simulation study for this particular case, we can conclude that Weibull distribution describes well the nature of the model concerned as compared to the exponential distribution in terms of the mean value of parameter estimates, bias, and the root means square error. …”
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Logistic regression methods for classification of imbalanced data sets
Published 2012“…These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. …”
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Optimisation of fed-batch fermentation process using deep reinforcement learning
Published 2023“…When dealing with model mismatch (±15% parameter variation in critical growth and maximum glucose uptake rate) and process disturbance (±20% deviation in substrate feeding concentration), the proposed algorithm was able to handle the changes with a minor effect on the yeast yield up to 13.78% and 2.52%, respectively, across all different initial condition cases. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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