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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Date July 31 2005-Aug. 4 2005

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  • 2005 IEEE International Joint Conference on Neural Networks (IJCNN)

    Publication Year: 2005, Page(s): 0_1
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  • Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005

    Publication Year: 2005, Page(s): i
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  • Copyright

    Publication Year: 2005, Page(s): ii
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  • IJCNN 2005: Session Grid

    Publication Year: 2005, Page(s):iii - vii
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  • Message from the General Chair

    Publication Year: 2005, Page(s):viii - x
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  • Organizing Committee / International Program Committee

    Publication Year: 2005, Page(s):xi - xiii
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  • 2005 International Neural Network Society Officers

    Publication Year: 2005, Page(s): xiv
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  • The INNS President's Welcome

    Publication Year: 2005, Page(s): xv
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  • Conference topics

    Publication Year: 2005, Page(s): xvi
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  • IEEE - CIS (Excom - Adcom)

    Publication Year: 2005, Page(s): xvii
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  • IEEE Computational Intelligence Society President's Welcome

    Publication Year: 2005, Page(s):xviii - xix
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (268 KB) | HTML iconHTML

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  • General information

    Publication Year: 2005, Page(s): xx
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  • Registration

    Publication Year: 2005, Page(s): xxi
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  • Florida Institute of Technology

    Publication Year: 2005, Page(s): xxiv
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  • Conference meeting rooms

    Publication Year: 2005, Page(s): xxv
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  • IJCNN 2005 Schedule-at-a-Glance

    Publication Year: 2005, Page(s):xxvi - xxvii
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  • The IJCNN 2005 Post-Conference Workshops

    Publication Year: 2005, Page(s): xxviii
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  • Table of contents

    Publication Year: 2005, Page(s):xxix - liv
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  • Issues in designing automated minimal resource allocation neural networks

    Publication Year: 2005, Page(s):2671 - 2673 vol. 5
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (452 KB) | HTML iconHTML

    Artificial neural networks (ANNs) have a long record of generally promising results in hydrology. The earlier applications were mainly based on the back propagation feedforward method, which often used a lengthy trial-and-error method to determine the final network parameters. An attempt to overcome this shortcoming of the traditional applications is the minimal resource allocation network (MRAN).... View full abstract»

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  • Wavelet networks: an alternative to classical neural networks

    Publication Year: 2005, Page(s):2674 - 2679 vol. 5
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1808 KB) | HTML iconHTML

    Artificial neural networks (ANNs) are being widely used to predict and forecast highly nonlinear systems. Recently, Wavelet networks (WNs) have been shown to be a promising alternative to traditional neural networks. In this study, the robustness of WNs and ANNs in modeling two distinct time series is investigated. The first series represents a chaotic system (Henon map) and the second series repr... View full abstract»

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  • Streamflow forecasting with uncertainty estimate using Bayesian learning for ANN

    Publication Year: 2005, Page(s):2680 - 2685 vol. 5
    Cited by:  Papers (1)
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    Accurate site-specific streamflow forecasts along with uncertainty estimate are of particular importance for water resources planning and management. In the last decade, different types of artificial neural network (ANN) models have been shown as promising alternative methods for rainfall-runoff modeling. However, one of the critical issues with ANN based modeling remains the lack of confidence li... View full abstract»

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  • Intelligent systems for meteorological events forecast

    Publication Year: 2005, Page(s):2686 - 2688 vol. 5
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    In this paper a committee of "intelligent systems" evaluates the occurrence of meteorological phenomena. Rain and fog are the events which are considered. The forecast system is based on a multinetwork approach which evaluates data coming from electronic sensors and from satellite observations. More data and more engines are used to increase the reliability of the event prediction. The increased c... View full abstract»

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  • A multilayer perceptron approach for the retrieval of vertical temperature profiles from satellite radiation data

    Publication Year: 2005, Page(s):2689 - 2693 vol. 5
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    In this paper a multilayer perceptron neural network is used to retrieve vertical atmospheric temperature profiles from satellite radiation data. The training set consists of data provided by the direct model characterized by the radiative transfer equation (RTE) and by real radiation data from the NOAA-HIRS/2 (high resolution infrared radiation) sounder. The retrieved vertical temperature profile... View full abstract»

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  • Machine learning in soil classification

    Publication Year: 2005, Page(s):2694 - 2699 vol. 5
    Cited by:  Papers (1)
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    In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc., intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori in... View full abstract»

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  • Estimation of prediction intervals for the model outputs using machine learning

    Publication Year: 2005, Page(s):2700 - 2705 vol. 5
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2128 KB) | HTML iconHTML

    A new method for estimating prediction intervals for a model output using machine learning is presented. In it, first the prediction intervals for in-sample data using clustering techniques to identify the distinguishable regions in input space with similar distributions of model errors are constructed. Then regression model is built for in-sample data using computed prediction intervals as target... View full abstract»

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