Get Artificial Intelligence and Soft Computing: 15th PDF

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

ISBN-10: 3319393774

ISBN-13: 9783319393773

ISBN-10: 3319393782

ISBN-13: 9783319393780

The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed lawsuits of the fifteenth overseas convention on synthetic Intelligence and smooth Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers awarded have been rigorously reviewed and chosen from 343 submissions. The papers integrated within the first quantity are geared up within the following topical sections: neural networks and their functions; fuzzy platforms and their purposes; evolutionary algorithms and their functions; agent structures, robotics and keep watch over; and trend class. the second one quantity is split within the following elements: bioinformatics, biometrics and scientific functions; info mining; man made intelligence in modeling and simulation; visible details coding meets desktop studying; and diverse difficulties of synthetic intelligence.

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Additional resources for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part I

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The weights of the output layer are plotted as a diagram with one row for each output neuron and one column for every hidden neuron in (L − 1)th layer. 40 40 1 2 6 30 1 2 6 30 20 20 10 10 0 -10 0 -20 -10 -30 -20 -40 -50 -20 -15 -10 -5 0 5 10 15 20 25 -30 -40 -30 -20 -10 (a) 0 10 20 30 40 (b) 40 40 1 2 6 30 20 30 1 2 6 20 10 0 10 -10 0 -20 -30 -10 -40 -20 -50 -60 -25 -20 -15 -10 -5 0 (c) 5 10 15 20 25 -30 -30 -20 -10 0 10 20 30 (d) Fig. 3. t-SNE projection [22] of 10D representations of reduced MNIST handwritten digits using (a) SAE (b) NCAE (c) L1 /L2 -NCAE (d) NMF.

In that case data from four cities was obtained: Cracow, Warsaw, Poznan and Wroclaw. Whole range of gathered data is between 1 January 2010 and 31 July 2014. It gives us 4,5 year of data which was divided between learning and testing set - three fourths of the set, counting from the beginning of the set, was used as the learning set, the rest was used as the testing set. MAPE was calculated for the testing set. In order to analyze the possibility of lowering MAPE value in electricity prediction we try to optimize data format of input values.

It shows the importance of electric usage that had place days before to predict near future. Table 6 is divided, much like in case before, into four sections. All of them contain three different numbers which represent size of input vector. However, each section has different value of neurons in hidden layer. Interpretation of the results here is difficult, much like in previous cases. General trend does not implicate any conclusions. We can notice that in some Neural System for Power Load Prediction in a Week Time Horizon 33 Table 6.

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Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part I by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada


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