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            <title xml:lang="en">Modeling Beach Rotation Using a Novel Legendre Polynomial Feedforward Neural Network Trained by Nonlinear Constrained Optimization</title>
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                <forename type="first">Anastasios</forename>
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                <title xml:lang="en">Modeling Beach Rotation Using a Novel Legendre Polynomial Feedforward Neural Network Trained by Nonlinear Constrained Optimization</title>
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                  <persName>
                    <forename type="first">Anastasios</forename>
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                  <date type="start">2016-09-16</date>
                  <date type="end">2016-09-18</date>
                  <settlement>Thessaloniki</settlement>
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                <editor>Lazaros Iliadis</editor>
                <editor>Ilias Maglogiannis</editor>
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                  <biblScope unit="volume">AICT-475</biblScope>
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                <term xml:lang="en">Nonlinear constrained optimization</term>
                <term xml:lang="en">Perched beach</term>
                <term xml:lang="en">Legendre polynomials</term>
                <term xml:lang="en">Feedforward neural network</term>
                <term xml:lang="en">Beach rotation</term>
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              <p>A Legendre polynomial feedforward neural network is proposed to model/predict beach rotation. The study area is the reef-fronted Ammoudara beach, located at the northern coastline of Crete Island (Greece). Specialized experimental devices were deployed to generate a set of input-output data concerning the inshore bathymetry, the wave conditions and the shoreline position. The presence of the fronting beachrock reef (parallel to the shoreline) increases complexity and imposes high non-linear effects. The use of Legendre polynomials enables the network to capture data non-linearities. However, in order to maintain specific functional requirements, the connection weights must be confined within a pre-determined domain of values; it turns out that the network’s training process constitutes a constrained nonlinear programming problem, solved by the barrier method. The performance of the network is compared to other two neural-based approaches. Simulations show that the proposed network achieves a superior performance, which could be improved if an additional wave parameter (wave direction) was to be included in the input variables.</p>
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          <date type="start">2016-11-07</date>
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              <addrLine>University Hill, 81100 Mytilene, Lesvos, Greece</addrLine>
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