Universidad de Guadalajara, Centro Universitario de los Lagos, Av. Enrique Díaz de León no. 1144 Col. Paseos de la Montaña, Lagos de Moreno, Jalisco, 47460, Mexico.
Neural Netw. 2012 Jul;31:81-7. doi: 10.1016/j.neunet.2012.03.005. Epub 2012 Mar 28.
A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.
提出了一种用于识别和控制非线性系统的时变学习算法，该算法基于递归高阶神经网络，集成了统计框架的使用。学习算法基于扩展卡尔曼滤波器，其中相关状态和测量噪声协方差矩阵由植物状态之间的耦合方差组成。该公式允许识别植物状态和神经收敛之间的关联相互作用。此外，还提出了一种基于滑动窗口的非平稳系统动态建模方法，以提高所提出方法中的神经识别。所提出方法的效率和准确性通过五自由度（DOF）机器人进行评估，其中基于时变神经识别器模型，使用分散式离散时间块控制和滑模技术为每个 DOF 设计独立控制器并开发轨迹跟踪。
