Extreme learning ANFIS for control applications
Extreme learning ANFIS for control applications
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DOI：
10.1109/cica.2014.7013226
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
G. N. Pillai;Jagtap Pushpak;M. Germin Nisha
中科院分区：
文献类型：
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作者：
G. N. Pillai;Jagtap Pushpak;M. Germin Nisha
本文提出了一种新的神经模糊学习机——极限学习自适应神经模糊推理系统（ELANFIS），它可以应用于非线性系统的控制。新的学习机结合了神经网络的学习能力和模糊系统的显式知识，就像传统的自适应神经模糊推理系统（ANFIS）一样。为了在不牺牲泛化能力的情况下获得更快的学习速度，ELANFIS的模糊层参数没有进行调整。所提出的学习机可用于非线性系统的逆控制和模型预测控制。仿真结果表明，该方法不仅提高了性能，而且减少了计算时间，这对实时控制至关重要。
This paper proposes a new neuro-fuzzy learning machine called extreme learning adaptive neuro-fuzzy inference system (ELANFIS) which can be applied to control of nonlinear systems. The new learning machine combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). The parameters of the fuzzy layer of ELANFIS are not tuned to achieve faster learning speed without sacrificing the generalization capability. The proposed learning machine is used for inverse control and model predictive control of nonlinear systems. Simulation results show improved performance with very less computation time which is much essential for real time control.