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The two-volume set LNAI 7267 and LNCS 7268 (together with LNCS 7269) constitutes the refereed lawsuits of the eleventh overseas convention on man made Intelligence and smooth Computing, ICAISC 2012, held in Zakopane, Poland in April/May 2012. The 212 revised complete papers awarded have been rigorously reviewed and chosen from 483 submissions. The papers are equipped in topical sections on neural networks and their purposes, laptop imaginative and prescient, photograph and speech research, info mining, implementation, bioinformatics, biometrics and clinical purposes, concurrent parallel processing, agent structures, robotics and regulate, synthetic intelligence in modeling and simulation, a variety of difficulties od synthetic intelligence.
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Extra resources for Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012, Proceedings, Part I
4 Main Results This section presents 7 experiments where the proposed model was used to train networks and ensembles of neural networks. On these experiments, ensembles of neural networks were used to classify customers of a Brazilian electricity distribution company (Light) as regular or irregular. 3) . Computer specification I is an Athlon 6000 with 1GB RAM and a GeForce 8400 GS; specification II is an Athlon 6000 with 2GB RAM and a GeForce 8800 GT; specification III is an Athlon 6000 with 2GB RAM; and a GeForce 260 GTX and specification IV is a Phenom II with 16GB RAM and four Tesla c1060.
LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006) 16. : Neuro-fuzzy Systems with Relation Matrix. M. ) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 210–215. Springer, Heidelberg (2010) 17. : Neuro-Fuzzy Systems of Type 2. In: Proc. 1st Int’l Conf. on Fuzzy Systems and Knowledge Discovery, Singapore, vol. 2, pp. 458–462 (2002) 18. : Interval type 2 neuro-fuzzy systems based on interval consequents. , Kacprzyk, J. ) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, Springer-Verlag Company, Heidelberg, New York (2003) 19.
Number of times cycles in a) classical, b) parallel implementation and c) performance factor classical/parallel Parallel Realisation of the Recurrent Multi Layer Perceptron Learning 19 The optimal performance of the structure is obtained by the speciﬁc way of sending weight values. 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.