仿真研究表明，SVM具有优良的逆模型辨识能力，基于模糊控制补偿的支持向量机逆控制系统的动态性能好、跟踪精度高、鲁棒稳定性强。
Simulations demonstrate that SVM has good nonlinear approximation capability for inverse model, and the proposed control system has good dynamic and static performances as well as good robustness.
基于神经网络动态逆方法，给出了一种非线性模型参考自适应跟踪控制方案。
A plan of model reference adaptive tracking control for nonlinear systems is introduced based on neural network dynamic inversion (NNDI).
从整体上对数字控制逆变焊机系统进行了仿真研究，提出用受控源的方法实现了动态电弧负载模型与系统的连接。
A system simulation study was done for full digital welding inverter, and a method of connecting dynamic arc load model to the system with controlled source was presented.
应用推荐