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algorithm steps » algorithm its (Expand Search)
phase function » taste function (Expand Search)
algorithm both » algorithm goa (Expand Search), algorithm aoa (Expand Search), algorithm its (Expand Search)
both function » cost function (Expand Search)
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Tracking analysis of the NLMS algorithm in the presence of both random and cyclic nonstationarities
Published 2003“…The results show that, unlike in the stationary case, the steady-state excess MSE is not a monotonically increasing function of the step size. Moreover, the ability of the adaptive algorithm to track the variations in the environment is shown to degrade with increasing frequency offset.…”
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Genetic and heuristic algorithms for regrouping service sites. (c2000)
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New enumeration algorithm for regular boolean functions
Published 2018“…After proving this equivalence, this paper introduces a novel data structure that may, with further tweaking, enable faster enumeration algorithms for both regular Boolean functions and all-capacities knapsack problem instances.…”
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5
A New Penalty Function Algorithm For Convex Quadratic Programming
Published 2020“…In this paper, we develop an exterior point algorithm for convex quadratic programming using a penalty function approach. …”
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Thyristor controlled phase shifter based stabilizer design usingsimulated annealing algorithm
Published 1999“…This paper presents a thyristor controlled phase shifter (TCPS) based stabilizer design using the simulated annealing (SA) algorithm. …”
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Rigorous Phase Equilibrium Calculation Methods for Strong Electrolyte Solutions: The Isothermal Flash
Published 2022“…In this way, an augmented function (Lagrange function) is formulated which serves as the basis for the equations that govern phase equilibrium. …”
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Adaptive step-size sign least mean squares
Published 2004“…A powerful adaptation scheme is used to adapt the step-size of the sign function inside the recursion of the sign algorithm. …”
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Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach
Published 2023“…The method is firstly developed in a centralized scheme and then extended to a distributed framework using appropriate asymptotically convergent consensus algorithms. Therefore, the asymptotic convergence of the designed distributed algorithm to the centralized one is guaranteed. …”
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Design and Implementation of an Advanced Control and Guidance Algorithm of a Single Rotor Helicopter
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doctoralThesis -
12
Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
Published 2024“…This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. …”
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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
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Distributed optimal coverage control in multi-agent systems: Known and unknown environments
Published 2024“…The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents’ coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. …”
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Computational evluation of protein energy functions
Published 2014“…However, the energy functions proposed so far by biophysicists and biochemists are still in the exploration phase and their usefulness has been demonstrated only individually. …”
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18
Iterative Least Squares Functional Networks Classifier
Published 2007“…Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. …”
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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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20
Tracking analysis of normalized adaptive algorithms
Published 2003“…Close agreement between analytical analysis and simulation results is obtained for the case of the NLMS algorithm. The results show that, unlike the stationary case, the steady-state excess-mean-square error is not a monotonically increasing function of the step-size, while the ability of the adaptive algorithm to track the variations in the environment degrades by increasing the frequency offset.…”
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