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Machine Theory

Artificial Intelligence and Soft Computing: 13th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard

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

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed complaints of the thirteenth overseas convention on synthetic Intelligence and tender Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers provided within the volumes, have been conscientiously reviewed and chosen from 331 submissions. The sixty nine papers integrated within the first quantity are occupied with the next topical sections: Neural Networks and Their purposes, Fuzzy structures and Their functions, Evolutionary Algorithms and Their purposes, type and Estimation, machine imaginative and prescient, photo and Speech research and distinct consultation three: clever tools in Databases. The seventy one papers within the moment quantity are prepared within the following topics: information Mining, Bioinformatics, Biometrics and scientific purposes, Agent structures, Robotics and regulate, man made Intelligence in Modeling and Simulation, quite a few difficulties of man-made Intelligence, certain consultation 2: computing device studying for visible details research and safety, distinctive consultation 1: purposes and homes of Fuzzy Reasoning and Calculus and Clustering.

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Extra 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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Ky = 0), the SPP wavevector kx or β is related to the optical frequency ω through the dispersion relation. εd εm εd + εm (4) εd εm εd + εm (5) kx = k0 β= ω c We take ω to be real and allow kx to be complex, since our main interest is in stationary monochromatic SPP fields in a finite area, where k0 = ω c (6) A Cascade NN Architecture Investigating Surface Plasmon Polaritons 25 is the wavevector in free space, and λ0 = ωc is the wavelength in vacuum. For metals, the permittivity is complex, which leads to kx being complex.

After that time point, the first simulation - the ART-2 network - continued working with a classical algorithm. 4. 4) with both low and high values of vibrations whereas it should be divided into two separated clusters. Similar phenomena was observed many times during the simulation. Table 1. Details of simulation process using the proposed hybrid system Time 1 50 71 91 214 743 747 977 3000 5869 6253 11158 14382 16385 16797 22429 24493 26266 Observed actions 1-st class-neuron was added to new opened area 1 2-nd class-neuron was added to area 1 3-td class-neuron was added to area 1 4-rh class-neuron was added to area 1 5-th class-neuron was added to area 1 6-th class-neuron was added to area 1 7-th class-neuron was added to area 1 8-th class-neuron was added to area 1 Borders of area 1 were determined and area 2 was opened 1-st class-neuron was added to area 2 2-nd class-neuron was added to area 2 3-td class-neuron was added to area 2 4-th class-neuron was added to area 2 Borders of area 2 were determined and area 3 was opened 1-st class-neuron was added to area 3 2-nd class-neuron was added to area 3 Borders of area 3 were determined and area 4 was opened 1-st class-neuron was added to area 4 The second simulation - the hybrid system - determined borders of the system internal areas.

Journal of Solar Energy Engineering 123, 327–332 (2001) 18. : Neural Networks. Academic Press, Warsaw (1993) (in Polish) 19. : DDtools, the Data Description Toolbox for Matlab (2013) The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks Jaroslaw Bilski1 , Jacek Smol¸ag1 , and Alexander I. ru Abstract. This paper presents the parallel architecture of the conjugate gradient learning algorithm for the feedforward neural networks. The proposed solution is based on the high parallel structures to speed up learning performance.

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