By Jerzy Świątek, Jakub M. Tomczak
This publication gathers the conscientiously reviewed court cases of the nineteenth foreign convention on structures technology, providing fresh examine findings within the parts of man-made Intelligence, computing device studying, Communication/Networking and knowledge know-how, keep an eye on idea, selection help, picture Processing and computing device imaginative and prescient, Optimization options, trend acceptance, Robotics, provider technology, Web-based providers, doubtful structures and Transportation structures. The overseas convention on structures technological know-how used to be held in Wroclaw, Poland from September 7 to nine, 2016, and addressed a number themes, together with structures concept, regulate thought, laptop studying, synthetic intelligence, sign processing, verbal exchange and data applied sciences, transportation platforms, multi-robotic structures and unsure platforms, in addition to their purposes. the purpose of the convention is to supply a platform for verbal exchange among younger and proven researchers and practitioners, fostering destiny joint learn in structures science.
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Extra info for Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016)
Fig. 4. Comparison of all tested predictive models (year prediction) Table 1. Obtained results of Mean Absolute Percentage Error (MAPE) Name of predictive model MAPE (%) month MAPE (%) year Additive Expert Ensembles (AddExp) 3,86 7,62 ARIMA (AR) 5,59 9,34 Dynamic Weighted Majority (DWM) 4,04 7,83 Dynamic Weighted Majority with decomp. (DWM +) 4,28 8,55 Exponential Smoothing (EXP) 5,71 9,30 Holt-Winters (HW) 6,45 9,88 Neural Network (NN) 4,97 9,10 Regression Tree (RT) 4,40 8,87 Support Vector Regression (SVR) 4,84 8,81 Based on further investigation of obtained predictions, we also provide a study on prediction error progress on previous one month interval.
The example presents a simpliﬁed case of a more general problem of objects’ action recognition and trajectories description (Wang et al. 2013). This may correspond in practice to monitoring (or back-monitoring) of selected individual vehicles in road trafﬁc. The universe has thus the form of a Cartesian product U ¼ S1 Â S2 Â . . Â SN ð4Þ consisting of KN N-tuples and the relation Ξ being to be recognized is described by the statement: “x 2Ξ iff x consists of elements representing the same real object” Solution of the problem consists thus in ﬁnding in U all N-tuples ω satisfying a relation Ξ of “representing the same real object”.
1 represents selected end-users from our dataset. The y axis shows an average time of execution for described time period. As we can see, the optimization by PSO algorithm helped the neural network to decrease the execution time for some end-users more than ﬁfteen times. It is important to mention that this time reduction also helped to improve the whole ensemble of predictive models. Then we continued with optimization of DWM method and its modiﬁed version. 44 R. Nemec et al. Fig. 1. Average execution time of prediction for 4 end-users using neural network optimization The Fig.