Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005

1. Front Matter

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2. New Neural Network Models

   1. Neuro-fuzzy Kolmogorov’s Network

      • Yevgeniy Bodyanskiy, Yevgen Gorshkov, Vitaliy Kolodyazhniy, Valeriya Poyedyntseva

      Pages 1-6

   2. A Neural Network Model for Inter-problem Adaptive Online Time Allocation

      • Matteo Gagliolo, Jürgen Schmidhuber

      Pages 7-12

   3. Discriminant Parallel Perceptrons

      • Ana González, Iván Cantador, José R. Dorronsoro

      Pages 13-18

   4. A Way to Aggregate Multilayer Neural Networks

      • Maciej Krawczak

      Pages 19-24

   5. Generalized Net Models of MLNN Learning Algorithms

      • Maciej Krawczak

      Pages 25-30

   6. Monotonic Multi-layer Perceptron Networks as Universal Approximators

      • Bernhard Lang

      Pages 31-37

   7. Short Term Memories and Forcing the Re-use of Knowledge for Generalization

      • Laurent Orseau

      Pages 39-44

   8. Interpolation Mechanism of Functional Networks

      • Yong-Quan Zhou, Li-Cheng Jiao

      Pages 45-51

3. Supervised Learning Algorithms

   1. Neural Network Topology Optimization

      • Mohammed Attik, Laurent Bougrain, Frédéric Alexandre

      Pages 53-58

   2. Rough Sets-Based Recursive Learning Algorithm for Radial Basis Function Networks

      • Yevgeniy Bodyanskiy, Yevgen Gorshkov, Vitaliy Kolodyazhniy, Irina Pliss

      Pages 59-65

   3. Support Vector Neural Training

      • Włodzisław Duch

      Pages 67-72

   4. Evolutionary Algorithms for Real-Time Artificial Neural Network Training

      • Ananda Jagadeesan, Grant Maxwell, Christopher MacLeod

      Pages 73-78

   5. Developing Measurement Selection Strategy for Neural Network Models

      • Przemysław Prętki, Marcin Witczak

      Pages 79-84

   6. Nonlinear Regression with Piecewise Affine Models Based on RBFN

      • Masaru Sakamoto, Dong Duo, Yoshihiro Hashimoto, Toshiaki Itoh

      Pages 85-90

   7. Batch-Sequential Algorithm for Neural Networks Trained with Entropic Criteria

      • Jorge M. Santos, Joaquim Marques de Sá, Luís A. Alexandre

      Pages 91-96

   8. Multiresponse Sparse Regression with Application to Multidimensional Scaling

      • Timo Similä, Jarkko Tikka

      Pages 97-102

   9. Training Neural Networks Using Taguchi Methods: Overcoming Interaction Problems

      • Alagappan Viswanathan, Christopher MacLeod, Grant Maxwell, Sashank Kalidindi

      Pages 103-108

   10. A Global-Local Artificial Neural Network with Application to Wave Overtopping Prediction

       • David Wedge, David Ingram, David McLean, Clive Mingham, Zuhair Bandar

       Pages 109-114

4. Ensemble-Based Learning

   1. Learning with Ensemble of Linear Perceptrons

      • Pitoyo Hartono, Shuji Hashimoto

      Pages 115-120

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

• Algorithms
• algorithm
• artificial intelligence
• bioinformatics
• computer vision
• intelligence
• learning
• learning theory
• modeling
• multimedia
• semantics
• speech processing
• speech recognition
• system modeling

• Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

  Włodzisław Duch

• Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

  Janusz Kacprzyk, Sławomir Zadrożny

• Adaptive Informatics Research Centre, Helsinki University of Technology, HUT, Finland

  Erkki Oja

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