Modular network SOM

This study aims to develop a generalized framework of an SOM called a modular network SOM (mnSOM). The mnSOM has an array structure consisting of functional modules that are trainable neural networks, e.g., multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM. In the case of MLP-modules, an mnSOM learns a group of systems or functions in terms of the input–output relationships in parallel with generating a feature map of them. Thus an mnSOM with MLP modules is an SOM in function space rather than in vector space. In this paper, first, as an example, we focus on a class of mnSOM that consists of MLP modules and introduce the architecture and algorithm. Then, a more generalized framework is described. Finally, some simulation results of an MLP-module-mnSOM are presented.