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This paper presents a hybrid visual servo controller for underwater vehicles by exploiting a combination of measured Euclidean information and image information of a single feature. A dynamic inversion-based neural network control scheme is proposed for tracking of the reference trajectory generated from a constant target pose. A single-hidden-layer (SHL) feedforward neural network, in conjunction...
Deep Belief Network (DBN) learns the features of the raw data automatically, and develops a new idea for the study of fault analysis of High Speed Train (HST). Combining deep learning and classification ensemble technology, this paper presents a novel DBN hierarchical ensemble model for HST fault analysis. Firstly, Fast Fourier Transform (FFT) coefficients of the HST vibration signals are extracted...
This paper aims at improving two main uncertain factors in neural networks training in developing a composite road safety performance indicator. These factors are the initial value of network weights and the iteration time. More specially, rough sets theory is applied for rule induction and feature selection in decision situations, and the concepts of reduct and core are utilized to generate decision...
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