Adaptive and Natural Computing Algorithms: Proceedings of by Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete

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 that 1993 and has a protracted heritage of selling the rules and realizing of computational intelligence paradigms in the clinical neighborhood 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 eventually Roanne, in France (2003), the ICANNGA sequence has demonstrated 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 the world over popular specialists. The 2005 convention, the 7th within the ICANNGA sequence, will occur on the college of Coimbra in Portugal, drawing at the event of earlier occasions, and following an identical basic version, combining technical periods, together with plenary lectures by means of popular scientists, with tutorials.

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

Example text

With zero mean, besides P(ft > 0) = P(& < 0). s. diverges if 23 ud > 1, and converges if ud < 1. In fact this algorithm can converge to a region near one of the zeros of (p. See [7] for details. 3 Batch Step Update and Backpropagation Using the above algorithm with feed forward neural networks is not possible since partial derivatives changes sign very frequently indicating a non smooth surface and this cause a fast step decrease and a very poor solution is reached. However, using constant step, one can observe two facts about a weight w.

1. The derivatives of Beta function We proved in [4, 5] that all derivatives of Beta function e L2(IR) and are of class C00. The general form of the nth derivative of Beta function is: "W dxn where: px(x)=-I. SL. x-xo and x\-x n\p n Pn(x)=(-l) r n\q The first (BW1), second (BW2) and third (BW3) derivatives of Beta wavelet are shown graphically in Figure 1. 2. Result of interpolation by Mexican hat. Fig, 1. First, second and third derivatives of Beta function. 2. Proposition if p = q , for all n e IN and 0 < n < p the functions dxn are wavelets [4, 5].

The main observation is that the algorithm with step update rule ud < 1 behaves better in 3 cases: digits {0,2,3}. The number of wrong classifications are very high for two reasons: small number of iterations and the described method of training makes harder for the net to classify with the simple structure. As a conclusion of this work we can say that a very 24 simple familly of algorithms for step update was presented and these simple results are encouraging. However, more work on these methods should be done.

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