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 overseas convention on man made Intelligence and smooth Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers offered have been conscientiously reviewed and chosen from 343 submissions. The papers incorporated within the first quantity are equipped within the following topical sections: neural networks and their functions; fuzzy structures and their functions; evolutionary algorithms and their functions; agent platforms, robotics and regulate; and development type. the second one quantity is split within the following elements: bioinformatics, biometrics and scientific purposes; info mining; man made intelligence in modeling and simulation; visible details coding meets computing device studying; and diverse difficulties of synthetic 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
196 Receptive ﬁelds (W(1) ) with weight histogram learned from MNIST digit data set using (a) SAE, (b) NCAE, (c) L1 /L2 -NCAE, and (d) NMF. Black pixels indicate negative, and white pixels indicate positive weights. Black nodes in (b) indicate neurons with zero weights. The range of weights are scaled to [−1, 1] and mapped to the graycolor map. w <= −1 is assigned to black, and w >= 1 is assigned to white color. (a) SAE (b) NCAE (c) L1 /L2 -NCAE Fig. 5. 196 decoding ﬁlters (W(2) ) with weight histogram learned from MNIST digit data set using (a) SAE (b) NCAE and (c) L1 /L2 -NCAE.
It can be observed in Fig. 8 that sparsiﬁcation of the output layer weights is the aftermath of the nonnegativity constraints imposed on the network. In addition, the patterns learned by neurons in each layer are localized, and this allows easy interpretation of what is going on inside the network. This is why we have a sparser, more localized weight distribution of hidden neurons which ﬁlters the distinctive part of the input image. Whereas in the case of SAE, the hidden neurons react to the whole image almost equally, and this makes it diﬃcult to have a glimpse of their inﬂuence in the classiﬁcation.
In order to analyze the possibility of lowering MAPE value in electricity prediction we try to optimize data format of input values. In this paper we aim at showing which format of same values gives the best result. A few parameters were examined as described below. Size of Input Vector. To get a working neural network we need to pass some values known as input vector. In this case our input contains parameters such as temperature, daily electricity usage, a day of the week and a day of the year.