By Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)
The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed complaints of the eighth overseas convention on Adaptive and traditional Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007.
The 178 revised complete papers provided have been rigorously reviewed and chosen from a complete of 474 submissions. The ninety four papers of the 1st quantity are prepared in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough platforms, simply as type and clustering. the second one quantity comprises eighty four contributions regarding neural networks, aid vector machines, biomedical sign and photo processing, biometrics, laptop imaginative and prescient, in addition to to manage and robotics.
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Extra info for Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II
42 N. Ishii, T. Deguchi, and M. Kawaguchi β B2 cell B1 cell Fig. 3. Schematic diagram of the stimulus movement from right to left C11 (λ) = h1 (λ) k2 β 2 C21 (λ1 , λ2 ) = 2 h1 (λ1 )h1 (λ2 ) . (α + kβ 2 )2 (14) (15) Similarly, the following equations are derived on the nonlinear pathway, C12 (λ) = βh1 (λ) C22 (λ1 , λ2 ) = h1 (λ1 )h1 (λ2 ) . (16) From (14) and (16), the ratio β is derived, which is abbreviated in the notation β= C12 C11 (17) and the following equation is derived C11 = C12 C21 C22 k C21 1− C11 2 +k C12 C11 2 .
E. for every number of features K, and the averaged values are displayed. Table 1. 9557472 Table 2. 9444444 The ﬁrst conclusion is that for the presented dataset the DCT2 transform performs better than DCT4, both in the ﬁxed variant (Tab. 1) and in the adapted one (Tab. 2), which is reﬂected in the lower error values and higher recognition rates. For both transforms we can observe that four DCT coeﬃcients contain enough information to enable successful recognition of almost all the investigated samples (recognition rate over 95%).
In the second variant the mFCT2 block is implemented as a fast orthogonal neural network. Exactly the same procedure for both variants is then applied with the mFCT4 algorithm (Fig. 3). 3 Fast Orthogonal Neural Network Construction The diagrams in Fig. 2, 3 serve as a starting point for the fast orthogonal neural networks design. They contain nodes with two inputs and two outputs, grouped into several layers, representing basic arithmetic operations on the processed data. Each basic operation may be presented in the form of multiplication: v y1 = P· 1 , y2 v2 (5) where the elements of the matrix P depend on the type of the operation.
Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II by Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)