Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Chatter free sliding mode control of a chaotic coal mine power grid with small energy inputs

An augmented proportional-integral sliding surface was designed for a sliding mode controller. A chatter free sliding mode control strategy for a chaotic coal mine power grid was developed. The stability of the control strategy was proven by Lyapunov stability theorem. The proposed sliding mode control strategy eliminated the chattering phenomenon by replacing the sign function with a saturation function, and by replacing the constant coefficients in the reaching law with adaptive ones. An immune genetic algorithm was used to optimize the parameters in the improved reaching law. The cut-in time of the controllers was optimized to reduce the peak energy of their output. Simulations showed that the proposed sliding mode controller has good, chatter flee performance.