通过工况的识别以及模型参数的离线辨识和在线优化，混合模型可以准确地模拟复杂过程在大范围内的动态特性。
By recognition of work condition, off-line identification and on-line optimization of parameters, hybrid model can be used to simulate dynamics of complex process correctly in a large scale.
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.
无论是理想的数学模型还是实际工业模型，计算机仿真结果均证明，将P ID型网络用于动态系统辨识具有更好的逼近效果。
Simulation results based on ideal mathematical model and industrial model show that the PID Elman network is prior to the modified Elman network in identification of nonlinear dynamic systems.
应用推荐