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By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

ISBN-10: 3319071726

ISBN-13: 9783319071725

ISBN-10: 3319071734

ISBN-13: 9783319071732

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed lawsuits of the thirteenth foreign convention on synthetic Intelligence and tender Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers awarded within the volumes, have been rigorously reviewed and chosen from 331 submissions. The sixty nine papers integrated within the first quantity are keen on the next topical sections: Neural Networks and Their purposes, Fuzzy structures and Their functions, Evolutionary Algorithms and Their purposes, class and Estimation, desktop imaginative and prescient, picture and Speech research and detailed consultation three: clever tools in Databases. The seventy one papers within the moment quantity are equipped within the following topics: facts Mining, Bioinformatics, Biometrics and scientific functions, Agent platforms, Robotics and keep watch over, synthetic Intelligence in Modeling and Simulation, quite a few difficulties of man-made Intelligence, certain consultation 2: computing device studying for visible info research and safeguard, distinctive consultation 1: functions and homes of Fuzzy Reasoning and Calculus and Clustering.

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Additional info for Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I

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In the presented parallel architecture each iteration requires only T Pl = Nl +2Nl−1 +m(Nl +2Nl−1 +21) time units (see Fig. 11) The factor m is the number of steps required to reach the directional minimum and to obtain the α∗ . Assuming m = 10 performance factor (P F = T Sl /T Pl ) of parallel realisation of the conjugate gradient learning algorithm achieves nearly 350 for Nl−1 = 100 inputs, Nl = 100 outputs in the l − th layer and it grows fast when these numbers grow, see Fig. 11. We observed Fig.

In the presented parallel architecture each iteration requires only T Pl = Nl +2Nl−1 +m(Nl +2Nl−1 +21) time units (see Fig. 11) The factor m is the number of steps required to reach the directional minimum and to obtain the α∗ . Assuming m = 10 performance factor (P F = T Sl /T Pl ) of parallel realisation of the conjugate gradient learning algorithm achieves nearly 350 for Nl−1 = 100 inputs, Nl = 100 outputs in the l − th layer and it grows fast when these numbers grow, see Fig. 11. We observed Fig.

Neural Networks and Their Applications, Zakopane, pp. 487–497 (1999) 26. : A type-1 approximation of interval type-2 FLS. , Petrosino, A. ) WILF 2009. LNCS, vol. 5571, pp. 287–294. Springer, Heidelberg (2009) 27. : Neural Networks, AOW RM (1993) (in Polish) 28. : Backpropagation through time: What it does and how to do it. Proceedings of the IEEE 78(10) (1990) 29. : Neural network learning without backpropagation. IEEE Transactions on Neural Networks 21(11), 1793–1803 (2010) ˙ 30. : Introduction to Artificial Neural Systems.

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Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada


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