By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada
The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed lawsuits of the 14th foreign convention on man made Intelligence and tender Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised complete papers provided within the volumes, have been rigorously reviewed and chosen from 322 submissions. those court cases current either conventional synthetic intelligence tools and delicate computing recommendations. The aim is to collect scientists representing either parts of analysis. the 1st quantity covers subject matters as follows neural networks and their purposes, fuzzy structures and their functions, evolutionary algorithms and their functions, type and estimation, machine imaginative and prescient, photo and speech research and the workshop: large-scale visible reputation and computer studying. the second one quantity has the point of interest at the following topics: information mining, bioinformatics, biometrics and scientific functions, concurrent and parallel processing, agent structures, robotics and keep watch over, synthetic intelligence in modeling and simulation and numerous difficulties of man-made intelligence.
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Extra resources for Artificial Intelligence and Soft Computing: 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I
Biological neurons use internal, plastic, and local adaptation mechanisms which enable them to represent frequently repeated combinations of similar input stimuli and connect these representations to reproduce their sequence. Current investigations in neurobiology     provide insight into universal plastic mechanisms which enable neurons to automatically change their connections and parameters to consolidate and represent frequent and similar combinations of object features and their sequences.
M. ) ICAISC 2013, Part I. LNCS (LNAI), vol. 7894, pp. 32–40. Springer, Heidelberg (2013) 7. : Parallel Structures for Feedforward and Dynamical Neural Networks (in Polish). AOW EXIT (2013) 8. : The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. M. ) ICAISC 2014, Part I. LNCS (LNAI), vol. 8467, pp. 12–21. Springer, Heidelberg (2014) 9. : Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks. 2357019 10. : The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks.
523–534. Springer, Heidelberg (2013) 21. : A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling. M. ) ICAISC 2013, Part I. LNCS (LNAI), vol. 7894, pp. 329–344. Springer, Heidelberg (2013) 22. : An algorithm for last-sqares estimation of nonlinear paeameters. J. Soc. Ind. Appl. , 431–441 (1963) 23. : Optimal training strategies for locally recurrent neural networks. Journal of Artiﬁcial Intelligence and Soft Computing Research 1(2), 103–114 (2011) 24.