Titlebook: Visual Information and Information Systems; 8th International Co Stéphane Bres,Robert Laurini Conference proceedings 2006 Springer-Verlag B

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Alexandra Teynor,Wolfgang Müller,Wolfgang Kowarschickline various time-varying problems. In comparison with conventional gradient neural networks (GNN), such RNN (or termed specifically as Zhang neural networks, ZNN) models are elegantly designed by defining matrix-valued indefinite error functions. In this paper, we generalize and investigate the ZNN

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Yan Yan,Jesse S. Jinechnique is exploited to decide whether there is a NLOS path from the base station (BS) to the mobile station (MS); Second, a biased Kalman filter (KF) is used to mitigate NLOS error in the raw measurements; and then, two measuring points which have maximum residual (so-called the upper dead point a

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Anelia Grigorova,Francesco G. B. De Nataleusing a weighted sum technique. Recurrent neural networks are then adopted to generate Pareto-optimal solutions. To diversify the solutions along Pareto fronts, particle swarm optimization is used to optimize the weights of the scalarized objective function. Numerical results are presented to illust

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Peter G. B. Enser,Christine J. Sandom,Paul H. Lewistoencoder feature selection (RAFS). This method based on autoencoder uses the radial basis function to achieve mapping instead of the weight. We also consider penalty to give a powerful constraint on redundant features. In extensive experiments, our method shows its outperformance in fair comparison

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