Designing dynamic temporally sensitive fuzzy neural networks

The concept of temporally sensitive fuzzy neural networks is introduced based on combining the basic ideas of logic-based neurocomputing with the concept of temporally sensitive connections of neural networks. This new class of neural networks helps address two main issues arising in time-dependent modeling environments. Firstly, these neural networks capture the underlying logical fabric of the problem and, secondly, they provide a useful insight into the temporal nature of the modeling environment. The authors show that the continuously changeable temporal environment gives rise to a logical transformation of the introduced model. This transformation is implemented by triggering from its original AND-like nature to an OR-like version, with this triggering regarded as a function of time. This paper discusses fuzzy decision-making in detail, particularly real estate problem solving.