Considering the correlations of the input indexes and the deficiency of calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support vector machine (PCA-GA-SVM) is proposed to improve the precision of bus arrival time prediction. And the No. 232 bus in Shenyang City of China is taken as an example. The traditional SVM and Kalman Filtering model and GA-SVM are also employed to make comparative analysis on the prediction rate, respectively. The result indicates that PCA-GA-SVM obtains more accurate prediction results of bus arrival time prediction.
Accurate prediction of bus arrival time is of great significance to improve passenger satisfaction and bus attraction. This paper presents the prediction model of bus arrival time based on support vector machine with genetic algorithm (GA-SVM). The character of the time period, the length of road, the weather, the bus speed and the rate of road usage are adopted as input vectors in Support Vector Machine (SVM), and the genetic algorithm search algorithm is combined to find the best parameters. Finally, the data from Bus No.249 in Shenyang, china are used to check the model. The experimental results show that the forecasting model is superior to the traditional SVM model and the Artificial Neural Network (ANN) model in terms of the same data, and is of higher accuracy, which verified the feasibility of the model to predict the bus arrival time.
We consider a class of evolution differential inclusions defining the so-called stop operator arising in elastoplasticity, ferromagnetism, and phase transitions. These differential inclusions depend on a constraint which is represented by a convex set that is called the characteristic set. For BV (bounded variation) data we compare different notions of BV solutions and study how the continuity properties of the solution operators are related to the characteristic set. In the finite-dimensional case we also give a geometric characterization of the cases when these kinds of solutions coincide for left continuous inputs.
Pro hloubavého čtenáře jsou v hlavní části článku shromážděna v historické posloupnosti fakta k zamyšlení se nad výše položenou otázku. V roce 2016 uplyne 170 roků od jednoho z nejvýznamněších činů lidského intelektu v historii nebeské mechaniky - objevu planety Neptun. Udál se za neobvyklých a částečně i dramatických okolností na různých mistech Evropy. Proběhl zásluhou úspěšného propojení matematicky obtížných a časově náročných výpočtů ve Francii, Anglii a krátkého pozorování v Německu., The discovery of planet Neptune is one of the greatest events in the history of celestian mechanics. Le Verrier and simultaneously Adam applied inverse perturbation theory to the problem of Uranus, whose irregularities in motion could be used for determination of the orbit and mass of a yet unknown planet. These very complicated computations were successfully finished in summer 1846. Neptune was discovered by Johann Galle using le Verrier‘s predictions in September of the same year proving that the mathematical methods and numerical calculations were not fundamentally flawed. Therefore, dicovery of Neptune cannot be considered as accidental., and Vladimír Štefl.
The recently announced results from the ICEP2 experiment are intepreted as an indirect observation of inflation gravitational waves. In this article we briefly discuss the cosmic inflation hypothesis, what is the B-mode, how it was measured, and why the polarization of the cosmic microwave background radiation can reveal how strong gravitational waves were present in the universe during this early epoch., Tomáš Ledvinka., and Obsahuje seznam literatury