By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada
The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed complaints of the fifteenth foreign convention on synthetic Intelligence and gentle Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers offered have been rigorously reviewed and chosen from 343 submissions. The papers integrated within the first quantity are equipped within the following topical sections: neural networks and their purposes; fuzzy platforms and their purposes; evolutionary algorithms and their functions; agent platforms, robotics and keep an eye on; and trend category. the second one quantity is split within the following components: bioinformatics, biometrics and clinical purposes; information mining; synthetic intelligence in modeling and simulation; visible details coding meets computing device studying; and diverse difficulties of man-made intelligence.
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Additional info for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part I
8. The weights of the a network trained using (a) SAE (b) L1 /L2 -NCAE. The weights of the softmax layer are plotted. Each row of the plot corresponds to each output neuron and each column for every (L − 1)th hidden neuron. The magnitude of the weight corresponds to the area of each square. Underneath the plot are the receptive ﬁelds learned from the reduced NORB dataset. The activations of (L−1)th -layer hidden neurons are depicted on the bar chart at the bottom of the plot. Table 1. Parameter settings for full MNIST and NORB Dataset SAE with Red.
We forecast the solar power using only information of the past of the solar power series, in other words we don’t use any other meteorological variables. 5, . . 9], Nx ∈ [30, 35, . . 15, . . 95]. Let Nx∗ , ρ∗ and α∗ be the best global parameters of an ESN according our empirical evaluations. 55 were in some cases unstable. 55. 2 Feature Selection Using SA Method We apply the SA method for automatically selecting other meteorological variables for forecasting the solar irradiance. We assume that several external variables impact in the solar irradiance, such as: air temperature, humidity, wind characteristics, etc.
Firstly, the most basic one, periodic value (day of the week and day of the year) is normalized to a closed interval [0, 1] and passed to the input vector. Secondly, days of the week (and analogously days of the year) are put on a circle in equal distances between the previous and the next day. We access those points by sine and cosine of a corresponding angle. In the third approach we use sine value of the current day. The most signiﬁcant advantage of that solution is continuity but an obvious disadvantage is that some distant days will have same values.