Download Adaptive and Natural Computing Algorithms: 8th International by Marek Krȩtowski, Marek Grześ (auth.), Bartlomiej PDF

By Marek Krȩtowski, Marek Grześ (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed lawsuits of the eighth overseas convention on Adaptive and typical 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 equipped in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough platforms, simply as category and clustering. the second one quantity includes eighty four contributions concerning neural networks, aid vector machines, biomedical sign and picture processing, biometrics, laptop imaginative and prescient, in addition to to regulate and robotics.

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Read Online or Download Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part I PDF

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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 I

Example text

1 Introduction The robustness of a design is defined as the maximum size of the deviation from this design that can be tolerated whereby the product still meets all requirements [1]. As an example, consider the temperature controller circuit [2] shown in Fig. 1. The performance function is: RT − on = R1R 2( E 2 R 4 + E1R 3) . R 3( E 2 R 4 + E 2 R 2 − E 1 R 2 ) We can evaluate, for example, what are the maximum possible deviations for each component in the Fig. 10 kΩ? That is, the goal is to assess the input parameters uncertainty, which maintains the output performance function within specified bounds.

The sample can be selected using a random or an importance procedure [15]. The process is repeated N times. The third approach is based on guaranteed numerical methods for approximating sets, such as the one suggested by Jaulin et al [16]. In this approach the region F∩B is approximated by covering it with simple non-overlapping subsets of ℜn, such as hyper-boxes. The procedure suggested in [16], called SIVIA (Set Inverter Via Interval Analysis), is able to determine two sets Xmin and Xmax such that Xmin ⊂ F∩B ⊂Xmax.

1, Gap = 5 %, Generations=100. 4 Centring Design Methodology Given the tolerance of each component, the optimal centring design to be considered consists on selecting the co-ordinates of centre of the hyper-box that maximises the yield that is the probability of satisfying the design specifications. Let B be the box defined by: B:= {x, C ∈ ℜn⏐xi ∈[Ci-Δxi, Ci+Δxi]}, where C is the unknown centre co-ordinates vector, and Δxi is the known tolerance for each variable. The designer formulates target values on the quality of the product by setting lower and upper bounds on the property yi(x).

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