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 considering the fact that 1993 and has a protracted historical past of marketing the foundations and figuring out of computational intelligence paradigms in the medical group and is a reference for demonstrated staff 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 demonstrated itself for skilled staff within the box. The sequence has additionally been of price to younger researchers wishing either to increase their wisdom and event and in addition to fulfill the world over popular specialists. The 2005 convention, the 7th within the ICANNGA sequence, will happen on the collage of Coimbra in Portugal, drawing at the adventure of prior occasions, and following an identical common version, combining technical classes, together with plenary lectures by means of popular scientists, with tutorials.

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

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The sequential discrete dynamics of such a network is here considered, in which the evolution of the network state y<*> = (j/f \ . . , y]p) G {0,1} S is determined for discrete time instants t = 0 , 1 , . . , as follows. g. y(°) = ( 0 , . . , 0). At discrete time t > 0, the excitation of any neuron j is defined as £J ' = Xw=i w(^J)Vi ~ h(j) including an integer threshold h(j) local to unit j . g. randomly) selected neuron j computes its new output yj = H(£j ) by applying the Heaviside activation function H, that is, j is active when H(£) = 1 for £ > 0 while j is passive when H(£) = 0 for f < 0.

302343 E-03 It. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 4 I Fig. 3. The capacitor and the inverse polarized diode equalization The expression (10) for current i(f) is obtained through following steps: (a) by equalizing capacity (C) voltage uc with an inverse polarized diode (D) voltage uj : (11) C that is ^0 (b) by differentiation the above equalization, we get: (13) (c) now, we are performing some transformations (formulae (14)-(17)): i'(t) CVT The capacitor and the diode equalization The basic question related to the proposed Hopfield neural network modification is undoubtedly: under which condition(s) the capacitor can be replaced with an inverse polarized diode?

28 Table 1. 29s) at most two cycles. In addition, it has been proved [3] that the classes of equivalent optimal stripifications of T are mapped one to one to the minimum energy states that are reached during any sequential computation by HT starting at the zero initial state (or %T can be initialized arbitrarily if one asymmetric weight is introduced). 4 Experiments A C++ program HTGEN has been created to automate the reduction from Section 3 including the simulation of Hopfield network %T using simulated annealing (see Section 2).