Showing 21 - 40 results of 87 for search '(( element data algorithm ) OR ((( b optimization algorithm ) OR ( neural coding algorithm ))))*', query time: 0.11s Refine Results
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    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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    Capturing outline of fonts using genetic algorithm and splines by Sarfraz, M.

    Published 2001
    “…We present a method to convert the original problem into a discrete combinatorial optimization problem and solve it by a genetic algorithm. …”
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    article
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    Fuzzy simulated evolution algorithm for VLSI cell placement by Sait, Sadiq M.

    Published 2003
    “…There has not been a consensus on how to accommodate all these (conflicting) requirements in the search for near optimal feasible solutions. In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. …”
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    article
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    A stochastic iterative learning control algorithm with application to an induction motor by Saab, Samer S.

    Published 2004
    “…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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    article
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    Stochastic P-type/D-type iterative learning control algorithms by Saab, Samer S.

    Published 2003
    “…This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. …”
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    article
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    XBeGene: Scalable XML Documents Generator by Example Based on Real Data by Harazaki, Manami

    Published 2012
    “…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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