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 court cases of the thirteenth foreign convention on man made Intelligence and gentle Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers provided within the volumes, have been rigorously reviewed and chosen from 331 submissions. The sixty nine papers incorporated within the first quantity are concerned about the next topical sections: Neural Networks and Their functions, Fuzzy platforms and Their purposes, Evolutionary Algorithms and Their purposes, category and Estimation, laptop imaginative and prescient, photograph and Speech research and distinctive consultation three: clever tools in Databases. The seventy one papers within the moment quantity are geared up within the following matters: info Mining, Bioinformatics, Biometrics and clinical purposes, Agent platforms, Robotics and keep watch over, man made Intelligence in Modeling and Simulation, numerous difficulties of synthetic Intelligence, specific consultation 2: laptop studying for visible details research and protection, precise consultation 1: purposes 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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This paper proposes a novel neural netwok (NN) topology to study of the problems of a SPP propagating at a metal flat interface separating dielectric medium. Currently, we are using NNs to study the inner relation between SPPs exciting wavelength, metal thickness and SPP wavelength and propagation length. The focus of this paper is on the determination of the dependance of the SPP propagation of the metal thickness employing suitable NN schematics. Due to the high sensitivity of the neural model to data oscillations a novel training procedure has been devised in order to avoid polarisations and miscorrections of some NN weights.

Application of Support Vector Machines, CNNs and DBNs 37 Deep Belief Network Hidden Layer Hidden Layer RBM RBM Visible Layer Visible Layer RBM Boltzmann Machine Restricted Boltzmann Machine Visible Layer Fig. 2. The structure of the general Boltzmann Machine, the RBM, and the DBN 3 Methodology For the object/image dataset, the small NORB [15] was used. The small NORB consists of 5 object categories and several thousand images per category, for a total of 24300 images each in the training and test sets.

1-10, pp. 1274–1277 (2006) 12. : Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6) (1994) 13. : Fuzzy regression modelling for tool performance prediction and degradation detection. International Journal of Neural Systems 20(5), 405–419 (2010) 14. : Conjugate Gradient Methods in Numerical Optimization, pp. 497–528. Springer, New York (2006) The Parallel Approach to the Conjugate Gradient Learning Algorithm 21 15. : Computational methods in optimization: a unified approach.

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