函数测试表明，改进后的算法使收敛速度显著加快，而且不易陷入局部极值点。
From experimental results it can be concluded that using a dynamic inertia weight makes the rapidity of convergence accelerate and is not easy to trap in the local extreme points.
仿真结果表明，改进BP神经网络PID使收敛变得更快，而且系统具有较强的鲁棒性和自适应能力。
The emulational results show that the improved BP neural network PID enables the convergence to be faster and the system has strong robustness and self-adaptive.
改进的CR法和CR列式法对UL列式法的局限进行了不同程度的改进，使收敛精度得到明显的改善。
The improved CR formulation and CR formulation can make some improvements to the above mantioned limitation, and greatly improve the accuracy of convergence.
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