Download Artificial Intelligence and Soft Computing: 11th by Krzysztof Bartecki (auth.), Leszek Rutkowski, Marcin PDF

By Krzysztof Bartecki (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)

The two-volume set LNAI 7267 and LNCS 7268 (together with LNCS 7269) constitutes the refereed court cases of the eleventh overseas convention on man made Intelligence and tender Computing, ICAISC 2012, held in Zakopane, Poland in April/May 2012. The 212 revised complete papers provided have been conscientiously reviewed and chosen from 483 submissions. The papers are prepared in topical sections on neural networks and their functions, laptop imaginative and prescient, picture and speech research, information mining, implementation, bioinformatics, biometrics and scientific functions, concurrent parallel processing, agent structures, robotics and keep watch over, synthetic intelligence in modeling and simulation, numerous difficulties od man made intelligence.

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Additional resources for Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012, Proceedings, Part I

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Besides the database size, there is also a tradeoff between the complexity of the algorithm used for training and the number of iterations needed to reach the network’s performance goal, which greatly affects the total training time. For example, if a neural network is trained by an algorithm of back propagation with gradient descent, the cost of each step is relatively small; however, many steps are required to train the network. On the other hand, a greater order gradient algorithm requires much less iterations, but with a much greater computational cost for each iteration [5].

68 0 5 10 15 Time [Days] 20 25 30 3 10 2 Amplitude 10 1 10 0 10 −1 10 −6 10 −5 10 −4 10 Frequencies [Hz] −3 10 Fig. 1. The light flux time-series and the relative power spectrum main sequence to red giants stars. 7 s, as light flux measurement and corrected flux estimation with the related absolute error. 2. The most common way to analyze a time-series and thus to derive the frequencies of oscillations is to convert the data acquired on the time domain to a set of values that range in a frequency domain (the Power Spectrum).

The practical structure for the first layer learning is shown in Fig. 9. Weights are sent in the following steps as indicated by the thick line in Fig. 10 The layout of all weights is identical in Fig. 10 and Fig. 4 which means that weights of the first layer, necessary for the calculations in the first and second layers can by sent fully paralelly. 3 Conclusion In this paper the parallel realisation of the RMLP neural network was proposed. We assume that all multiplications and additions operations take the same time unit.

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