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.)

The ICANNGA sequence of meetings has been organised seeing that 1993 and has an extended heritage of marketing the foundations and realizing of computational intelligence paradigms in the medical neighborhood and is a reference for confirmed employees during this sector. 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 at last Roanne, in France (2003), the ICANNGA sequence has confirmed itself for skilled 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 fulfill across the world popular specialists. The 2005 convention, the 7th within the ICANNGA sequence, will happen on the college of Coimbra in Portugal, drawing at the adventure of earlier occasions, and following an identical basic version, combining technical periods, together with plenary lectures through popular scientists, with tutorials.

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Springer Verlag, pp. 95-124. [3] P. Cristea, R. Tuduce, and A. Cristea, (2000) Time Series Prediction with Wavelet Neural Networks, Proceedings of IEEE Neural Network Applications in Electrical Engineering, pp. 5-10. [4] W. Bellil, C. Ben Amar et M. A. Alimi, (2003) Beta Wavelet Based Image Compression, International Conference on Signal, System and Design, SSD03, Tunisia, Mars, vol. 1, pp. 77-82. [5] W. Bellil, C. Ben Amar, M. Zaied and M. A. Alimi, (2004) La fonction Beta et ses derivees: vers une nouvelle famille d'ondelettes, First International Conference on Signal, System and Design, SCS'04, Tunisia, Mars, vol.

2003) General-purpose computation with neural networks: A survey of complexity theoretic results. Neural Computation 75(12): 2727-2778. , Sejnowski, TJ. (1985) A learning algorithm for Boltzmann machines. Cognitive Science 9(1): 147-169. 29 The Linear Approximation Method to the Modified Hopfieid Neural Network Parameters Analysis S. I. Bauk1, S. M. Perovich2, A. 9\iF (Figure 2). By these replacements, the classical Hopfieid model analysis come to be moved from the field of linear differential equations to the field of exponential equations solvable easily by the linear approximation method proposed throughout the next sections.

001 (that is l/(large fan in), and Td = 10" 7 . 0001. This two values where randomly set. Figure 1 describes the minimum wrong values on the test set in all the 5 x 12000 iterations. 0001 (u = d = ud= 1). with 1. w > 1 andO < d < 1; 2. 7 T is bounded by F^ and F u constants; 3. 7o some constant that could be much less than F u since step can grow. After the step update rule we define the weight update rule. It is known that online training has been shown to produce better solutions than weight batch update.

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