Predicting evolving chaotic time series with fuzzy neural networks

This work tackles the seldom discussed task of predicting chaotic time series generated by dynamic systems with evolving parameters. Representative chaotic time series produced by different system dimensions are introduced with a critical parameter linearly depending on time. The evolving character of systems are qualitatively studied by phase portraits. We assess the predictability of different fuzzy neural network (FNN) architectures on several evolving chaotic time series. Experiments illustrate that FNN models can generally better approximate evolving chaotic systems comparing to the autoregression method as a benchmark. The main contribution of our work is that we found out certain FNN types, e.g., NEFCON and DENFIS, are more robust to changing system parameters. In spite of the performance, some FNN models are more vulnerable and incline to be destabilized by high order chaotic systems. This work also casts light on composing FNN structures to capture evolving characters of chaotic time series in the future.