Download e-book for iPad: Artificial Intelligence and Soft Computing: 14th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard

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

ISBN-10: 3319193236

ISBN-13: 9783319193236

ISBN-10: 3319193244

ISBN-13: 9783319193243

The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed lawsuits of the 14th overseas convention on man made Intelligence and delicate Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised complete papers offered within the volumes, have been rigorously reviewed and chosen from 322 submissions. those lawsuits current either conventional synthetic intelligence equipment and gentle computing recommendations. The aim is to collect scientists representing either parts of study. the 1st quantity covers issues as follows neural networks and their purposes, fuzzy structures and their purposes, evolutionary algorithms and their purposes, type and estimation, machine imaginative and prescient, photograph and speech research and the workshop: large-scale visible popularity and desktop studying. the second one quantity has the focal point at the following matters: info mining, bioinformatics, biometrics and clinical functions, concurrent and parallel processing, agent platforms, robotics and regulate, synthetic intelligence in modeling and simulation and numerous difficulties of man-made intelligence.

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Additional resources for Artificial Intelligence and Soft Computing: 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I

Example text

8. The general structure for parallelization of the QR decomposition Fig. 9. The processing elements of the QR decomposition 10 J. Bilski et al. the elements of the A matrix and elements H2 transform the elements of the h vector. This step performs a sequence of the Householder reflection so as to reset the elements below the main diagonal of the A matrix. First, the elements in the first column are reset, then the second and so on, until the last but one. The A matrix and the h vector are transformed.

Connections and synaptic weights between as-neurons enable to differentiate various influences of the context formed by the activity of other presynaptic neurons. This allows to produce different context-sensitive reactions of a neural network constructed from Innovative Types and Abilities of Neural Networks 29 as-neurons. Furthermore, associative consolidation of representations of various sequences in a single neural graph sometimes triggers generalized reactions that have not been trained (Fig.

The construction process of ANAKGs has been precisely described in [11], [12], and [14]. 3 Generalization of Training Sequences This section describes an experiment in which a training sequence set called ’Monkey’: ”I have a monkey. My monkey is very small. It is very lovely. It likes to sit on my head. It can jump very quickly. It is also very clever. It learns quickly. ” was used to develop an ANAKG graph shown in Fig. 2. New as-neurons represented each new word. Words from each sentence were presented with an interval of 20 units of simulation time.

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Artificial Intelligence and Soft Computing: 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada


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