Fuzzy cognitive maps (FCMs) integrate neural networks and fuzzy logic to
model complex nonlinear problems through causal reasoning. Interval-valued
FCMs (IVFCMs) have recently been proposed to model additional uncertainty in
decision-making tasks with complex causal relationships. In traditional
FCMs, optimization algorithms are used to learn the strengths of the
relationships from the data. Here, we propose a novel IVFCM with real-coded
genetic learning. We demonstrate that the proposed method is effective for
predicting corporate financial distress based on causally connected
financial concepts. Specifically, we show that this method outperforms FCMs,
fuzzy grey cognitive maps and adaptive neuro-fuzzy systems in terms of root
mean squared error.
Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress
