يعرض 21 - 40 نتائج من 137 نتيجة بحث عن '(( algorithm both function ) OR ((( algorithm growth function ) OR ( algorithm cost function ))))', وقت الاستعلام: 0.13s تنقيح النتائج
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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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    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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    article
  6. 26

    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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    conferenceObject
  7. 27

    On the Optimization of Band Gaps in Periodic Waveguides حسب Jamil Renno (14070771)

    منشور في 2025
    "…<h3 dir="ltr">Purpose</h3><p dir="ltr">This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. The purpose of this work is to provide a systematic comparison of various nature-inspired algorithms across both low-cost analytical and high-fidelity simulation-driven design problems.…"
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    Stochastic evolution algorithm for technology mapping حسب Al-Mulhem, A.S.

    منشور في 1998
    "…SELF-Map is based on the Stochastic Evolution (SE) algorithm. The state space model of the problem is defined and suitable cost function which allows optimization for area, delay, or area-delay combinations is proposed. …"
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    article
  9. 29

    Evolutionary Algorithms for VLSIMultiobjective Netlist Partitioning حسب Sait, Sadiq M.

    منشور في 2006
    "…Fuzzy rules are incorporated in order to handle the multiobjective cost function. For SimE, fuzzy goodness functions are designed for delay and power, and proved efficient. …"
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    article
  10. 30

    A new multimodulus blind equalization algorithm حسب Abrar, Shafayat

    منشور في 2004
    "…The proposed algorithm is obtained by removing the discontinuity found in the RCA cost function. …"
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    article
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    A new genetic algorithm approach for unit commitment حسب Mantawy, A.H.

    منشور في 1997
    "…A fitness function is constructed from the total operating cost of the generating units without penalty terms. …"
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    article
  13. 33

    Evolutionary algorithms for VLSI multi-objective netlist partitioning حسب Sait, Sadiq M.

    منشور في 2006
    "…Fuzzy rules are incorporated in order to handle the multi-objective cost function. For SimE, fuzzy goodness functions are designed for delay and power, and proved efficient. …"
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    article
  14. 34

    An Evolutionary Algorithm for the Allocation Problem in High-Level Synthesis حسب Harmanani, Haidar

    منشور في 2005
    "…The method performs allocation of functional units, registers, and multiplexers in addition to controller synthesis with the objective of minimizing the cost of hardware resources. …"
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    article
  15. 35

    Simulated evolution algorithm for multiobjective VLSI netlist bi-partitioning حسب Sait, Sadiq M.

    منشور في 2003
    "…Fuzzy rules are used in order to design the overall multi-objective cost function that integrates the costs of three objectives in a single overall cost value. …"
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    article
  16. 36

    A genetic-based algorithm for fuzzy unit commitment model حسب Mantawy, A.H.

    منشور في 2000
    "…In the implementation for the GA, coding of the UCP solutions is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units plus a penalty term determined due to the fuzzy load and spinning reserve membership functions. …"
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    article
  17. 37

    New fault models and efficient BIST algorithms for dual-portmemories حسب Amin, A.A.

    منشور في 1997
    "…These modifications allow multiple access of memory cells for increased test speed with minimal overhead on both silicon area and device performance. New fault models are proposed, and efficient O(n) test algorithms are described for both the memory array and the address decoders. …"
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    article
  18. 38

    Economic load dispatch using memetic sine cosine algorithm حسب Abu Zitar, Raed

    منشور في 2022
    "…In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. …"
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  19. 39

    Evolutionary algorithms, simulated annealing and tabu search: a comparative study حسب Youssef, H.

    منشور في 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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    article
  20. 40

    Convergence analysis of the variable weight mixed-norm LMS-LMFadaptive algorithm حسب Zerguine, A.

    منشور في 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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    article