Barreto G A, Araujo A R
Dept. of Teleinformatics Eng., Fed. Univ. of Ceara, Fortaleza-CE, Brazil.
IEEE Trans Neural Netw. 2004 Sep;15(5):1244-59. doi: 10.1109/TNN.2004.832825.
In this paper, we introduce a general modeling technique, called vector-quantized temporal associative memory (VQTAM), which uses Kohonen's self-organizing map (SOM) as an alternative to multilayer perceptron (MLP) and radial basis function (RBF) neural models for dynamical system identification and control. We demonstrate that the estimation errors decrease as the SOM training proceeds, allowing the VQTAM scheme to be understood as a self-supervised gradient-based error reduction method. The performance of the proposed approach is evaluated on a variety of complex tasks, namely: i) time series prediction; ii) identification of SISO/MIMO systems; and iii) nonlinear predictive control. For all tasks, the simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network. The SOM has also shown to be less sensitive to weight initialization than MLP networks. We conclude the paper by discussing the main properties of the VQTAM and their relationships to other well established methods for dynamical system identification. We also suggest directions for further work.
在本文中，我们介绍了一种通用的建模技术，称为矢量量化时间关联记忆（VQTAM），它使用科霍宁自组织映射（SOM）作为多层感知器（MLP）和径向基函数（RBF）神经模型的替代方案，用于动态系统识别和控制。我们证明，随着SOM训练的进行，估计误差会减小，这使得VQTAM方案可被理解为一种基于自监督梯度的误差减少方法。所提出方法的性能在各种复杂任务上进行了评估，即：i）时间序列预测；ii）单输入单输出/多输入多输出（SISO/MIMO）系统识别；以及iii）非线性预测控制。对于所有任务，SOM产生的仿真结果与MLP网络产生的结果一样准确，并且优于RBF网络产生的结果。SOM还显示出比MLP网络对权重初始化的敏感性更低。我们通过讨论VQTAM的主要特性及其与其他成熟的动态系统识别方法的关系来结束本文。我们还提出了进一步工作的方向。
