Evolving ensemble of fuzzy models

This paper presents an online learning-based neuro fuzzy system called evolving Fuzzy Ensemble (eFE). The hierarchical computational structure of eFE is progressively adapted to autonomously support fuzzy data associations in accordance with neurophysiological studies. Activity-dependent synapse with global decay learning rule is incorporated to simulate the retention and active forgetting mechanisms that are involved in memory persistence. Such features incorporated in eFE model make it suitable to address the nonstationary characteristics of real-world problems. This work demonstrates the use of simple mechanisms to accomplish complex form of associative learning, an idea that has been suggested by psychologists for many years but has only recently been verified at the cellular level. The proposed eFE model is evaluated and compared with other modelling techniques in two benchmark time series experiments. The experimental results demonstrate the capabilities, and illustrate the viability of the proposed modelling technique.