IEEE Trans Neural Netw Learn Syst. 2020 Apr;31(4):1297-1309. doi: 10.1109/TNNLS.2019.2919676. Epub 2019 Jun 24.
In this paper, a full-regulated neural network (NN) with a double hidden layer recurrent neural network (DHLRNN) structure is designed, and an adaptive global sliding-mode controller based on the DHLRNN is proposed for a class of dynamic systems. Theoretical guidance and adaptive adjustment mechanism are established to set up the base width and central vector of the Gaussian function in the DHLRNN structure, where six sets of parameters can be adaptively stabilized to their best values according to different inputs. The new DHLRNN can improve the accuracy and generalization ability of the network, reduce the number of network weights, and accelerate the network training speed due to the strong fitting and presentation ability of two-layer activation functions compared with a general NN with a single hidden layer. Since the neurons of input layer can receive signals which come back from the neurons of output layer in the output feedback neural structure, it can possess associative memory and rapid system convergence, achieving better approximation and superior dynamic capability. Simulation and experiment on an active power filter are carried out to indicate the excellent static and dynamic performances of the proposed DHLRNN-based adaptive global sliding-mode controller, verifying its best approximation performance and the most stable internal state compared with other schemes.
本文设计了一种具有双隐层递归神经网络（DHLRNN）结构的全调节神经网络（NN），并提出了一种基于 DHLRNN 的自适应全局滑模控制器，用于一类动态系统。建立了理论指导和自适应调整机制，以设置 DHLRNN 结构中高斯函数的基宽和中心向量，根据不同的输入，可以自适应地将六组参数稳定到最佳值。新的 DHLRNN 可以提高网络的准确性和泛化能力，减少网络权重的数量，并通过与具有单个隐层的一般 NN 相比，双层激活函数具有更强的拟合和表示能力，从而加快网络训练速度。由于输出反馈神经结构中输入层的神经元可以接收来自输出层的神经元的反馈信号，因此它可以具有联想记忆和快速的系统收敛性，从而实现更好的逼近和优越的动态性能。对有源电力滤波器进行了仿真和实验，结果表明，所提出的基于 DHLRNN 的自适应全局滑模控制器具有优异的静态和动态性能，与其他方案相比，它具有最佳的逼近性能和最稳定的内部状态。
