By Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete Ribeiro, Dr. Rudolf F. Albrecht, Dr. Andrej Dobnikar, Dr. David W. Pearson, Dr. Nigel C. Steele (eds.)

ISBN-10: 3211249346

ISBN-13: 9783211249345

ISBN-10: 3211273891

ISBN-13: 9783211273890

The ICANNGA sequence of meetings has been organised on the grounds that 1993 and has a protracted heritage of marketing the rules and knowing of computational intelligence paradigms in the medical neighborhood and is a reference for demonstrated employees during this zone. beginning in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague within the Czech Republic (2001) and eventually Roanne, in France (2003), the ICANNGA sequence has validated itself for knowledgeable staff within the box. The sequence has additionally been of worth to younger researchers wishing either to increase their wisdom and adventure and likewise to satisfy across the world well known specialists. The 2005 convention, the 7th within the ICANNGA sequence, will happen on the college of Coimbra in Portugal, drawing at the event of prior occasions, and following an analogous common version, combining technical periods, together with plenary lectures by means of well known scientists, with tutorials.

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Extra info for Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005

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2, pp. 227-236. [9] Y. Oussar, I. Rivals, L. Personnaz (1998) Training Wavelet Networks for Nonlinear Dynamic InputOutput Modeling Neurocomputing. Chang, Weihui Fu, Minjun Yi, (1998) Short term load forecasting using wavelet networks Engineering Intelligent Systems for Electrical Engineering and Communications 6, 217-23. Echauz, (1998) Strategies for Fast Training of Wavelet Neural Networks, 2nd International Symposium on Soft Computing for Industry, 3rd World Automation Congress, Anchorage, Alaska, May 10-14, 1-6.

Advances in Neural Information Processing Systems 8: 24-30 18 Beta wavelet networks for function approximation Wajdi BELLIL1, Chokri BEN AMAR2 and Adel M. org Abstract Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators. In this paper we present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D function approximation. Our purpose is to approximate an unknown function f: Rn -> R from scattered samples (xt; y{ - f(x)) i=l n, where: S we have little a priori knowledge on the unknown function f which lives in some infinite dimensional smooth function space, S the function approximation process is performed iteratively: each new measure on the function (x;; f(Xj)) is used to compute a new estimate f as an approximation of the function f.

While the Sigma-if neuron is only a simple computational model, not designed for modeling biological neurons, it would nevertheless be interesting to research the relations between the properties of Sigma-if networks and those of biological processing systems. 7 Acknowledgements The above research in the area of intelligent information systems and machine learning is funded by the Polish Ministry of Scientific Research and Information Technology within the 4 T11C 02425 research grant. This support is gratefully acknowledged.

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Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005 by Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete Ribeiro, Dr. Rudolf F. Albrecht, Dr. Andrej Dobnikar, Dr. David W. Pearson, Dr. Nigel C. Steele (eds.)


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