Zhu Xueli

Application of Dynamic Dead Time Control in High Frequency & Voltage Power Supply

In this paper, phase-shift controlled full-bridge series resonant high frequency & voltage power is used for discharge plasma. It adds dynamic zero-voltage delay control scheme which can provide optimal turn-on delay timing regardless of input voltage, output load or component tolerances. When the expected zero voltage condition is reached, a switching transition is commanded to make converter implement ZVS on the whole condition. Practical application results indicated it can reduce power consumption and high surge voltage and current brought by inverter due to unreasonable dead time and thus improve the reliability of the power components. The efficiency and performance also increased greatly.

Robust fuzzy control for Uncertain Nonlinear System based on multi-objective optimization

This paper deals with the problem of robust control for Uncertain Nonlinear System via fuzzy control approach. First, a fuzzy controller with robust control capability is proposed. For avoiding complex controller parameters adjustment, Multi-objective particle swarm Optimization (PSO) algorithm is adopted during the controller design. Secondly, the multi-model is employed to represent the nonlinear system with norm-bounded parameter uncertainties. Finally, a simulation example illustrates the effectiveness and the feasibility of the proposed approach.

The comparison with improved mixture kernel SVM and traditional neural network

Support Vector Machines bases on statistical learning theory and replace the minimization experiential risk minimization by structural risk minimization, thus have large advantage over the traditional neural network on small sample set for classification. Related documents and experimental data prove that SVM is the best learning machine among all kinds recently and has large advantage over those of traditional neural networks. In this paper we prove that the performance of an improved SVM with mixed kernel will make the advantage more obviously. Different from some papers choose kernels and parameters randomly, we choose the kernels for SVM theoretically, through observing and computing the kernel matrix. Base on this, we used the selected kernel functions to get a new mixed kernel function. Experiential data proved that this new SVM has a better performance than that of that traditional neural network. This will give us a method to get a new learning machine for pattern identification.