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This paper presents a kernel-based extreme learning machine (KELM) modeling method for speed decision making of autonomous land vehicles (ALVs) on rural roads. The model is obtained offline via the KELM algorithm using a small number of typical samples collected by an ALV platform on rural roads from experienced drivers. Compared with other typical machine learning algorithms such as support vector...
In this paper, an algorithm applied for road extraction on SAR image is proposed, which is based on a multi-scale linear feature detector and beamlet framework, and then a quadratic kernel is introduced to offer optimal representation for the circle roads, aiming at improving the extraction quality. Firstly, a multi-scale pyramid is built on the input image and at each level the image is subdivided...
To solve the learning control problem of a bioreactor system, a novel framework of heuristic dynamic programming (HDP) with sparse kernel machines is presented, which integrates kernel methods into critic learning of HDP. As a class of adaptive critic designs (ACDs), HDP has been used to realize online learning control of dynamical systems, where neural networks are commonly employed to approximate...
As a benchmark control problem with nonlinearity and instability, the controller design for inverted pendulums becomes more difficult when there are model uncertainties and unknown disturbances in the plant dynamics. In this paper, a kernel-based reinforcement learning controller is developed for inverted pendulums with unknown dynamics and stochastic disturbances. The learning controller makes use...
In this paper, Walter extended the classical Shannon sampling theorem to some wavelet subspaces. We generalize the sampling theorem for multi-wavelet subspaces to multi-wavelet subspaces with a two-scaling function of Multi Resolution Analysis (MRA) based on reproducing kernel Hilbert space. Then the reconstruction function can be expressed by multi-wavelet with two scaling function of MRA using frame...
With the fast development of robotics and intelligent vehicles, there has been much research work on modeling and motion control of autonomous vehicles. However, due to model complexity, and unknown disturbances from dynamic environment, the motion control of autonomous vehicles is still a difficult problem. In this paper, a novel self-learning path-tracking control method is proposed for a car-like...
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