Abstract
The ultimate challenges of system modeling concern designing accurate yet highly transparent and user-centric models. We have witnessed a plethora of neurofuzzy architectures which are aimed at addressing these two highly conflicting requirements. This study is concerned with the design and the development of transparent logic networks realized with the aid of fuzzy neurons and fuzzy unineurons. The construction of networks of this form requires a formation of efficient interfaces that constitute a conceptually appealing bridge between the model and the real-world experimental environment in which the model is to be used. In general, the interfaces are constructed by invoking some form of granulation of information; and binary (Boolean) discretization, in particular. We introduce a new discretization environment that is realized by means of particle swarm optimization (PSO) and data clustering implemented by the K-Means algorithm. The underlying structure of the network is optimized by invoking a combination of the PSO and the mechanisms of conventional gradient-based learning. We discuss various optimization strategies by considering Boolean as well as fuzzy data coming as the result of discretization of original experimental data and then involving several learning strategies. We elaborate on the interpretation aspects of the network and show how those could be strengthened through efficient pruning. We also show how the interpreted network leads to a simpler and more accurate logic description of the experimental data. A number of experimental studies are included. (C) 2009 Elsevier B.V. All rights reserved.