Generic Hebbian ordering-based fuzzy rule base reduced neuro-fuzzy system with fuzzy rule interpolation (RS-Hebb+) - Yan, Hongxu (2017)
Neuro-fuzzy system, traditionally used in dynamic data sets modelling, is now evolving rapidly in both structure and style, including the trends from offline system changing to online system, and increasingly more concepts added to address issues like data sparsity, time-variants and non-linearity. As theoretical researches go on, the results have also been applied in a wild range of industries, including traffic control, rainfall prediction and financial markets. Nevertheless, most of the systems now existing are not capable enough to dealing with sparse and dynamic time series data, which can be widely found in stock markets. Besides the other popular techniques introduced to address this issue, interpolation and extrapolation are the ones that gives most promising results and potential improvements on the existing system.