This NNSC model utilizes the optimized method that combines the gradient and multiplicative algorithm to learn the feature coefficients, and only the gradient algorithm to learn feature vectors.
该模型利用梯度和倍增因子相结合的优化算法实现特征系数的学习；
The gradient algorithm was adopted to calculate the required control force to suppress the rotor's instantaneous response coming from the parameter variance in the stiffness varying control of rotor.
为了解决转子变刚度控制中因参数变化导致瞬态响应问题，采用梯度算法，计算出抑制转子瞬态响应所需的控制力。
Simulation results show that the natural gradient approach has faster convergence speed and better separation performance than the conventional gradient based algorithm.
仿真结果显示，自然梯度算法比传统梯度算法收敛速度更快，分离效果更好。
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