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 complaints of the 14th overseas convention on man made Intelligence and gentle Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised complete papers provided within the volumes, have been conscientiously reviewed and chosen from 322 submissions. those lawsuits current either conventional man made intelligence equipment and gentle computing suggestions. The target is to compile scientists representing either parts of analysis. the 1st quantity covers subject matters as follows neural networks and their functions, fuzzy structures and their purposes, evolutionary algorithms and their functions, class and estimation, machine imaginative and prescient, snapshot and speech research and the workshop: large-scale visible reputation and desktop studying. the second one quantity has the focal point at the following topics: information mining, bioinformatics, biometrics and scientific functions, concurrent and parallel processing, agent structures, robotics and keep an eye on, synthetic intelligence in modeling and simulation and numerous difficulties of man-made intelligence.

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**Sample text**

Otherwise, the μ value should be increased β times and the algorithm goes back to step 3. 5. The algorithm terminates when the gradient falls below a preset value or the goal function falls below a preset value. 2 Parallel Realisation First, the errors in all neurons using backpropagation are calculated assuming that each time only one error is given to the output and than the Jacobian matrix is determined. This is accomplished by the structure shown in Fig. 4. Its processing elements are shown in Fig.

11–16. Springer, Heidelberg (2008) 4. : Parallel realisation of the recurrent Elman neural network learning. M. ) ICAISC 2010, Part II. LNCS (LNAI), vol. 6114, pp. 19–25. Springer, Heidelberg (2010) 5. : Parallel realisation of the recurrent multi layer perceptron learning. M. ) ICAISC 2012, Part I. LNCS (LNAI), vol. 7267, pp. 12–20. Springer, Heidelberg (2012) 6. : Parallel approach to learning of the recurrent Jordan neural network. M. ) ICAISC 2013, Part I. LNCS (LNAI), vol. 7894, pp. 32–40.

1], [7]. In the future research we plan to design parallel realisation of learning of other structures including probabilistic neural networks [27] and various fuzzy [11], [17], [20], [35], [36], [40], and neuro-fuzzy structures [12], [13], [16], [18], [21], [30]. Fig. 11. Number of times cycles in a) classical (serial), b) parallel implementation and c) performance factor References 1. : The UD RLS algorithm for training the feedforward neural networks. International Journal of Applied Mathematics and Computer Science 15(1), 101–109 (2005) 12 J.