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In this paper, we propose an Extreme Learning Machine (ELM) approach for solving large and complex data problems. In contrast to existing approaches, we embed hidden nodes that are designed using Restricted Boltzmann machine (RBM) into the classical ELM, exhibiting excellent generalization performances. To overcome the high computational complexity involved especially on large datasets, hidden nodes...
Recent advances in the traffic monitoring systems have made traffic velocity information accessible in real time. This paper proposes a supervised predictive energy management framework aiming to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) by incorporating dynamic traffic feedback data. Compared with conventional model predictive control (MPC), an additional supervisory...
Predicting the future speed of the ego-vehicle is a necessary component of many Intelligent Transportation Systems (ITS) applications, in particular for safety and energy management systems. In the last four decades many parametric speed prediction models have been proposed, the most advanced ones being developed for use in traffic simulators. More recently non-parametric approaches have been applied...
Load balancing is a challenging work for parallel dynamic programming due to its intrinsically strong data dependency. Two issues are mainly involved and equally important, namely, the partitioning method as well as scheduling and distribution policy of subtasks. However, researchers take into account their load balancing strategies primarily from the aspect of scheduling and allocation policy, while...
In this paper, a control model is developed to automate the process of navigation in rat-robot-a new type of bio-robot based on BCI(Brain-Computer Interface) technique. Because of the particular difficulties in rat-robot control, we design a novel control model to ‘learn’ and ‘imitate’ the control behavior of human guidance. General Regression Neural Network (GRNN) model is used to analyze the control...
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