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    Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems

    机译:小波模糊神经网络在动态系统辨识与预测控制中的应用。

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    This paper presents a wavelet fuzzy neural network (WFNN) structure for identifying and controlling nonlinear dynamic systems. The proposed WFNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rules. A training algorithm adopting a gradient descent method is employed to identify the unknown parameters in the WFNN. For the control problem, a WFNN-based predictive control (WFNNPC) law is derived via a generalized predictive performance criterion, and the control algorithm is proven to guarantee the convergence of the WFNNPC controller. The conditions of the stability analysis of the resulting control system are presented based on the Lyapunov stability theorem. Finally, the WFNN is applied in numerical simulations and experiments (identification and control of nonlinear dynamic systems and a physical positioning mechanism). The results confirm the effectiveness of the WFNN.
    机译:本文提出了一种用于识别和控制非线性动力学系统的小波模糊神经网络(WFNN)结构。所提出的WFNN是基于一组模糊规则构建的。每个规则在规则的后续部分都包含一个小波函数。采用梯度下降法的训练算法识别WFNN中的未知参数。针对控制问题,通过广义的预测性能准则推导了基于WFNN的预测控制律(WFNNPC),并证明了该控制算法能够保证WFNNPC控制器的收敛性?;贚yapunov稳定性定理,给出了所得控制系统稳定性分析的条件。最后,WFNN用于数值模拟和实验(非线性动态系统的识别和控制以及物理定位机制)。结果证实了WFNN的有效性。

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