يعرض 1 - 20 نتائج من 112 نتيجة بحث عن '(((( algorithm steps function ) OR ( algorithm phase function ))) OR ( algorithm both function ))', وقت الاستعلام: 0.12s تنقيح النتائج
  1. 1

    Tracking analysis of the NLMS algorithm in the presence of both random and cyclic nonstationarities حسب Moinuddin, M.

    منشور في 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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    article
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    New enumeration algorithm for regular boolean functions حسب Nasrallah, Walid F.

    منشور في 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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    A New Penalty Function Algorithm For Convex Quadratic Programming حسب Bendaya, M.

    منشور في 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 حسب Abido, M.A.

    منشور في 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 حسب Ilias K. Nikolaidis (9217172)

    منشور في 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 حسب Aldajani, M.A.

    منشور في 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 حسب Nargess Sadeghzadeh-Nokhodberiz (16904952)

    منشور في 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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    Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization حسب Abu Zitar, Raed

    منشور في 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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    Distributed optimal coverage control in multi-agent systems: Known and unknown environments حسب Mohammadhasan Faghihi (22303057)

    منشور في 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 حسب Mansour, Nashat

    منشور في 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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    Iterative Least Squares Functional Networks Classifier حسب Faisal, Kanaan A

    منشور في 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 حسب Nasr, G.E.

    منشور في 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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    Tracking analysis of normalized adaptive algorithms حسب Moinuddin, M.

    منشور في 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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    article