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algorithm time » algorithm its (Expand Search), algorithm sma (Expand Search)
where function » heart function (Expand Search)
algorithm 1 » algorithm _ (Expand Search), algorithm a (Expand Search), algorithms _ (Expand Search)
1 function » _ functional (Expand Search)
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Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach
Published 2006Get full text
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Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach
Published 2023“…<p dir="ltr">The problem of formation tracking control for a group of quadcopters with nonlinear dynamics using Barrier Lyapunov Functions (BLFs) is studied in this paper where the quadcopters are following a desired predefined trajectory in a predefined formation shape. …”
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Genetic and heuristic algorithms for regrouping service sites. (c2000)
Published 2000Get full text
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Salp swarm algorithm: survey, analysis, and new applications
Published 2024“…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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Evolutionary algorithms, simulated annealing and tabu search: a comparative study
Published 2020“…The three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search frominitial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time (iteration count), and (4) the number of solutions found at successive intervals of the cost function. …”
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Convergence analysis of the variable weight mixed-norm LMS-LMFadaptive algorithm
Published 2000“…In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares-least mean fourth) adaptive algorithm is derived. The proposed algorithm minimizes an objective function defined as a weighted sum of the LMS and LMF cost functions where the weighting factor is time varying and adapts itself so as to allow the algorithm to keep track of the variations in the environment. …”
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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
Published 2017Get full text
doctoralThesis -
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Time-varying volatility model equipped with regime switching factor: valuation of option price written on energy futures
Published 2025“…We develop a semi-analytical method to determine the price of European options on these energy futures, involving the derivation of the characteristic function for the energy futures' dynamics. To determine the parameters of the regime switching model and identify when economic states change, we employ the EM algorithm, utilizing real gas futures price data. …”
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New Fast Arctangent Approximation Algorithm for Generic Real-Time Embedded Applications
Published 2019“…A new 2nd order rational approximation formula is introduced for the first time in this work and benchmarked against existing alternatives as it improves the new algorithm performance. …”
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Cross entropy error function in neural networks
Published 2002“…The ANN is implemented using the cross entropy error function in the training stage. The cross entropy function is proven to accelerate the backpropagation algorithm and to provide good overall network performance with relatively short stagnation periods. …”
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conferenceObject -
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Optimization of Support Structures for Offshore Wind Turbines using Genetic Algorithm with Domain-Trimming (GADT)
Published 2017“…The two versions of the optimization problem are nonlinearly constrained where the objective function is the material weight of the supporting truss. …”
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Evolutionary algorithm for protein structure prediction
Published 2010“…A protein is characterized by its 3D structure, which defines its biological function. The protein structure prediction problem has real-world significance where several diseases are associated with the wrong folding of proteins. …”
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An improved kernelization algorithm for r-Set Packing
Published 2010“…We present a reduction procedure that takes an arbitrary instance of the r -Set Packing problem and produces an equivalent instance whose number of elements is in O(kr−1), where k is the input parameter. Such parameterized reductions are known as kernelization algorithms, and a reduced instance is called a problem kernel. …”
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A fuzzy basis function network for generator excitation control
Published 1997“…The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on generator loading conditions. The orthogonal least squares learning algorithm is developed for designing an adequate and parsimonious FBFN model. …”
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