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 foreign convention on man made Intelligence and smooth 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 all for the next topical sections: Neural Networks and Their purposes, Fuzzy structures and Their purposes, Evolutionary Algorithms and Their purposes, type and Estimation, desktop imaginative and prescient, photograph and Speech research and targeted consultation three: clever equipment in Databases. The seventy one papers within the moment quantity are geared up within the following matters: facts Mining, Bioinformatics, Biometrics and scientific purposes, Agent platforms, Robotics and keep watch over, synthetic Intelligence in Modeling and Simulation, a number of difficulties of man-made Intelligence, distinctive consultation 2: desktop studying for visible info research and defense, distinct consultation 1: functions and homes of Fuzzy Reasoning and Calculus and Clustering.
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Were used to compute the errors ea , eb and e∗ as in (15). The training has been organised in four diﬀerent activities, executed on an OpenMP environment (see Fig. 5). The ﬁrst activity, named NN Simulation, provides as input to the whole cascade neural network with a training pattern, which has been previously generated. The second activity, named Phase A, and started once the ﬁrst activity has terminated, uses a gradient descent algorithm to adjust the neural weights of the intermediate layer II and the ﬁrst neuroprocessing (layers IIIa and IVa).
1. Geometry for SPP propagation at a single interface between metal and dielectric Fig. 2. Implemented geometry in COMSOL dedicated NN. Unfortunately, such training sessions would result in very timeconsuming computation. In order to overcome the above mentioned problem, this paper proposes a novel parallel paradigm for training that manages to run a single comprehensive training for the cascade NN as a whole, thus avoiding separate training phases. This novel solution has been used for the problem at hand, described in Section 2.