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    ESANN
    2000
    159views Neural Networks» more  ESANN 2000»
    Bootstrap for neural model selection
    14 years 24 days ago
    Bootstrap for neural model selection
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    Riadh Kallel, Marie Cottrell, Vincent Vigneron
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    ESANN
    2000
    78views Neural Networks» more  ESANN 2000»
    A statistical model selection strategy applied to neural networks
    14 years 24 days ago
    A statistical model selection strategy applied to neural networks
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    In statistical modelling, an investigator must often choose a suitable model among a collection of viable candidates. There is no consensus in the research community on how such a...
    Joaquín Pizarro Junquera, Elisa Guerrero V&...
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    ESANN
    2000
    113views Neural Networks» more  ESANN 2000»
    Iterative learning neural network control for nonlinear system trajectory tracking
    14 years 24 days ago
    Iterative learning neural network control for nonlinear system trajectory tracking
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    Ping Jiang, Rolf Unbehauen
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    ESANN
    2000
    87views Neural Networks» more  ESANN 2000»
    A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model
    14 years 24 days ago
    A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model
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    Using a nonlinear 15-state helicopter model in 6 DOF, two di erent neural control systems, both acting as rate damping, have been designed and compared. They are both based on the ...
    Piero A. Gili, Manuela Battipede
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    ESANN
    2000
    118views Neural Networks» more  ESANN 2000»
    Committee formation for reliable and accurate neural prediction in industry
    14 years 24 days ago
    Committee formation for reliable and accurate neural prediction in industry
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    Abstract. This paper describes "cranking", a new committee formation algorithm. Cranking results in accurate and reliable committee predictions, even when applied to comp...
    Peter J. Edwards, Alan F. Murray
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    ESANN
    2000
    116views Neural Networks» more  ESANN 2000»
    Self-Organisation in the SOM with a finite number of possible inputs
    14 years 24 days ago
    Self-Organisation in the SOM with a finite number of possible inputs
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    Abstract. Given a one dimensional SOM with a monotonically decreasing neighbourhood and an input distribution which is not Lebesque continuous, a set of su cient conditions and a T...
    John A. Flanagan
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    ESANN
    2000
    145views Neural Networks» more  ESANN 2000»
    Load forecasting dealing with medium voltage network reconfiguration
    14 years 24 days ago
    Load forecasting dealing with medium voltage network reconfiguration
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    Planing the operation in modern power systems requires suitable anticipation of load evolution at different levels of distribution network. Under this perspective, load forecasting...
    José Nuno Fidalgo, João Abel Pe&cced...
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    ESANN
    2000
    95views Neural Networks» more  ESANN 2000»
    Influence of weight-decay training in input selection methods
    14 years 24 days ago
    Influence of weight-decay training in input selection methods
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    We describe the results of a research on the effect of weight-decay (WD) in input selection methods based on the analysis of a trained multilayer feedforward network. It was propos...
    Mercedes Fernández-Redondo, Carlos Hern&aac...
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    ESANN
    2000
    129views Neural Networks» more  ESANN 2000»
    Discriminative learning for neural decision feedback equalizers
    14 years 24 days ago
    Discriminative learning for neural decision feedback equalizers
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    In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of ...
    Elio D. Di Claudio, Raffaele Parisi, Gianni Orland...
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    ESANN
    2000
    100views Neural Networks» more  ESANN 2000»
    Regularization in oculomotor control
    14 years 24 days ago
    Regularization in oculomotor control
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    In modelling the development of the oculomotor control system using neural networks, it is important to determine the appropriate cost function on which to train the models. Whilst...
    John A. Bullinaria, Patricia M. Riddell
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