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

ISBN-10: 3642293468

ISBN-13: 9783642293467

ISBN-10: 3642293476

ISBN-13: 9783642293474

The two-volume set LNAI 7267 and LNCS 7268 (together with LNCS 7269) constitutes the refereed lawsuits of the eleventh overseas convention on synthetic Intelligence and smooth Computing, ICAISC 2012, held in Zakopane, Poland in April/May 2012. The 212 revised complete papers offered have been rigorously reviewed and chosen from 483 submissions. The papers are equipped in topical sections on neural networks and their purposes, machine imaginative and prescient, photo and speech research, facts mining, implementation, bioinformatics, biometrics and scientific purposes, concurrent parallel processing, agent platforms, robotics and keep watch over, synthetic intelligence in modeling and simulation, numerous difficulties od synthetic intelligence.

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Additional info 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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Structure of the RMLP network Fig. 2. Recal phase of the RMLP network and the structures of processing elements 14 J. Bilski and J. Smolag Parallel realisation of the recall phase algorithm uses architecture which requires many simple processing elements. The parallel realisation of the RMLP network in recal phase is depicted in Fig. 2a and its processing elements in Fig. 2b. Four kinds of functional processing elements take put in the proposed solution. The aim of PE (A) is to delay inputs and outputs signals, so that values of signals appear on inputs of network from previous instances.

4a weight vectors of individual neurons are plotted, whereas Fig. 4b shows original step responses (solid line), obtained by the numerical solution of (14) and the responses of the GHA-PCA approximation model (dashed line), compared for six different spatial positions l. Furthermore, Fig. 5 shows approximation results analogous to those presented in Fig. 4, obtained as a result of the use of the FF-PCA method discussed in Sec. 2. As can be seen, in contrast to the case of the GHA-PCA network, the weight vectors w1 , w2 and w3 of the projection layer of the FF-PCA network are not orthogonal – their values have somewhat ”chaotic” distribution.

In addition, ANEEL is treating the issue as a priority and intends to adopt a policy of no tolerance [3], forcing companies to clearly specify goals for reducing these losses. Currently, Light uses a set of methodologies and is associated with a reporting service to help identify low-voltage customers suspected of committing any type of fraud. These customers are classified as suspect by these methodologies and a specialist company selects a particular set of customers to be inspected. Through this process, Light has obtained an average of 25% successes in the verification of fraudulent customers [3].

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Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012, Proceedings, Part I by Krzysztof Bartecki (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)


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