Fatehi Alireza, Abe Kenichi
Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Int J Neural Syst. 2008 Jun;18(3):233-56. doi: 10.1142/S0129065708001555.
The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.
作者提出的多模型单输出映射（MMSOM）识别方法，通过使用不规则自组织映射（ISOM）改进为基于不规则自组织映射的多模型（MMISOM）。神经网络的输入是在每个时刻自适应计算的瞬时模型的参数。神经网络学习这些模型。其输出节点的参考向量是局部模型参数的估计值。在每个时刻，选择输出与工厂输出最接近的模型作为工厂模型。本文中使用的ISOM是由所有节点以及它们之间的一些加权链接构成的图，以形成最小生成树图。本文表明，如果初始模型数量少于合适数量，则有可能添加新模型。当空间为凹形时，MMISOM在覆盖工厂的线性模型空间方面表现出更大的灵活性。
