  This paper proposes a new self-organizing interval type-2 fuzzy neural
network with multiple outputs (SOIT2FNN-MO) for multi-step time series
prediction. Differing from the traditional six-layer IT2FNN, a nine-layer
network is developed to improve prediction accuracy, uncertainty handling and
model interpretability. First, a new co-antecedent layer and a modified
consequent layer are devised to improve the interpretability of the fuzzy model
for multi-step predictions. Second, a new transformation layer is designed to
address the potential issues in the vanished rule firing strength caused by
highdimensional inputs. Third, a new link layer is proposed to build temporal
connections between multi-step predictions. Furthermore, a two-stage
self-organizing mechanism is developed to automatically generate the fuzzy
rules, in which the first stage is used to create the rule base from empty and
perform the initial optimization, while the second stage is to fine-tune all
network parameters. Finally, various simulations are carried out on chaotic and
microgrid time series prediction problems, demonstrating the superiority of our
approach in terms of prediction accuracy, uncertainty handling and model
interpretability.
