聚类算法将高斯基函数神经网络补偿器与模糊控制相结合应用于磁轴承系统

Chao-Ting Chu, H. Chiang, Yung-Sheng Chang
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引用次数: 5

摘要

提出了基于模糊控制的高斯基函数神经网络补偿器的聚类算法。非线性MBS改进了传统的轴承摩擦损失,采用模糊控制器和神经网络的非线性系统不需要精确的MBS数学模型。我们在神经网络中使用了模糊c均值和k均值调整高斯基函数的聚类算法。最后,利用Lyapunov稳定性来保证MBS的收敛性,实验结果表明该算法在MBS中具有满意的性能。
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Clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system
This paper proposed clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system (MBS). The nonlinear MBS improved traditional bearing friction losses, and nonlinear system with fuzzy controller and neural network does not require precise MBS mathematical model. We used clustering algorithms which are fuzzy c-means and k-means adjusted Gaussian basis function in neural network. Finally, we used the Lyapunov stability to guarantee MBS convergence, and the experimental results shows proposed algorithm has satisfactory performance in MBS.
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