Search Results - (( parameter information modeling algorithm ) OR ( java application customization algorithm ))
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Orthogonal least square algorithm and its application for modelling suspension system
Published 2001“…Orthogonal least square estimation is an algorithm which can determine the structure of a model by identifying the significant terms contributed to the model and also capable of providing the final values of parameter estimates. …”
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Characterization of PV panel and global optimization of its model parameters using genetic algorithm
Published 2023“…This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. …”
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Characterization of PV panel and global optimization of its model parameters using genetic algorithm
Published 2013“…This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. …”
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Experimenting the dendrite cell algorithm for disease outbreak detection model
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Parameter-driven count time series models / Nawwal Ahmad Bukhari
Published 2018“…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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Symmetric Key Size for Different Level of Information Classification
Published 2006“…Our proposed model includes a lifespan of information to be encrypted. …”
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Modified key size determination models for symmetric ciphers
Published 2005“…Key is an important element in cryptographic algorithms since it is one of the parameters that determine the security level of the algorithms. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…Firefly algorithm outperformed the other metaheuristic algorithms used to solve this proposed hybrid artificial intelligence model regarding parameter sensitivity. …”
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Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
Published 2020“…On bias evaluation, these criteria only tackle on the number of parameters in a model. There scarcely have been any information criterion that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. …”
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A comparative study for parameter selection in online auctions
Published 2009“…Self adaptation genetic algorithm is the last model that will be used to evolve the bidding strategy. …”
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Discrete-Time System Identification Based On Novel Information Criterion Using Genetic Algorithm
Published 2017“…Parameter magnitude-based information criterion 2 (PMIC2), as a novel information criterion, is used alongside Akaike information criterion (AIC). …”
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Augmented model based double iterative loop techniques for integrated system optimisation and parameter estimation of large scale industrial processes
Published 1993“…The double iterative loop structures of the proposed algorithms use the real process measurement within the outer loops while the inner loops involve model based computation only. …”
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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying
Published 2019“…In the on-line learning model, the recursive kernel is also successfully used to generate a fixed reservoir with optimized information by dissipating and overwriting the information from the coming data in the learning part of the prediction model, which is helpful for improvement of forecasting performance. …”
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Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti
Published 2022“…This study extracted pothole area and volume information from fusion of Digital Elevation Model (DEM) and classified MS image. …”
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Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
Published 2005“…The vagueness and uncertainty of the parameters are represented in the model construction, as a way of increasing the available information in order to achieve a more precise model of reality. …”
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