KEYWORDS: Convolution, Data modeling, Feature extraction, Radar signal processing, Radar, Education and training, Detection and tracking algorithms, Signal processing
Aiming at the problems of low accuracy and poor timeliness of conventional radiation source recognition, a radar radiation source recognition method based on random convolution kernel and Stacking is proposed in this paper. The one-dimensional sequence radiation source signal is normalized, the random convolution check sequence signal is used for efficient feature extraction transformation, and the transformed feature data is trained and learned by XGboost model. Finally, the trained model is used to complete the recognition of radiation source. The experimental results show that this method has better timeliness and higher recognition rate than the traditional radiation source identification method, and has reference significance for engineering implementation.
Trajectory Tracking is a hot topic in robot manipulators control. The robot manipulator is a classical nonlinear and multibody system. Control of such a system is difficult to acquire the pleasant result. This paper explores an efficient control for a 2-DOF robot manipulator using modified sliding mode control to overcome the nonlinearity and time-varying parameters. The novel control scheme integrates the conventional sliding mode control with Zeroing Neural Network to accelerate the convergence time and suppress the chattering. The simulation experiments are conducted to show the superiority and validity of the proposed method. The results demonstrate the proposed method presents the higher accuracy than classical sliding mode control with chattering suppress as well.
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