Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers.
Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers.
复制标题
模糊规则模拟网络，基于非线性离散时间控制器的强化学习。
DOI：
10.1016/j.isatra.2008.07.001
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发表时间：
2008
期刊：
影响因子：
7.3
通讯作者：
C. Treesatayapun
中科院分区：
文献类型：
--
作者：
C. Treesatayapun
本文介绍了一种基于多输入模糊规则仿真网络（MIFRENs）的自调节网络的非线性离散系统自适应控制器及其强化学习算法。由于MIFREN的通用函数近似，第一个MIFREN称为MIFRENcis，用于估计长期成本函数，该函数作为调优过程的性能指标。另一个网络或mifrenis是通过定义的If-Then规则通过人类知识设计的直接控制器。对于任何系统参数的选择过程，如学习率和一些常数参数，用所提出的定理的证明来表示。通过选定的非线性离散时间系统进行计算机仿真，并与其他控制器的结果进行比较，验证了系统的性能。
This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFRENcis used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFRENais designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system’s performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development.