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

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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm حسب De Rochechouart, Maxence

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Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function حسب Mujahid, S.N.

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Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function حسب Duffuaa, S. O.

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ANT-colony optimization-direct torque control for a doubly fed induction motor : An experimental validation حسب Said Mahfoud (17150968)

"…The new combined ACO-DTC strategy has been studied for optimizing the gains of the PID controller by using a cost function such as Integral Square Error (ISE). …"

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

"…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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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System UsingA Time-Multiplied Cost Function حسب Boukas, EK

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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function حسب Boukas, EK

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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function حسب Boukas, EK

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

"…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. …"

Efficient Algorithms for Constructing Preset Distinguishing Sequences for Nondeterministic Finite State Machines حسب Haddad, Abdul Rahim

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Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem حسب Abu Zitar, Raed

"…In particular, a new modified method based on the Arithmetic Optimization Algorithm is proposed. The optimization is applied to a formulated cost function that considers uncertainty, false alarms, and existing clutters. …"
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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques حسب Fares, Samar

"…An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. …"
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A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities حسب Mahdi Mokhtarzadeh (11593310)

"…Three objective functions are considered to minimize costs, noise pollutions, and the harassment caused by the establishment of a hub for people, a new objective that locates hubs in less populated areas. …"

Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach حسب Nargess Sadeghzadeh-Nokhodberiz (16904952)

"…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. …"

Design and Implementation of an Advanced Control and Guidance Algorithm of a Single Rotor Helicopter حسب Al-Omari, Muhannad A. R. I.

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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids حسب Khayata, Mohammad Tarek

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Neural Networks as a Convex Problem حسب Khalil, Baha

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Iterative Least Squares Functional Networks Classifier حسب Faisal, Kanaan A

"…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.

"…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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