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.

翻译：本文提出了一种新型多输出自组织区间二型模糊神经网络（SOIT2FNN-MO），用于多步时间序列预测。与传统的六层IT2FNN不同，本文开发了一个九层网络以提高预测精度、不确定性处理能力和模型可解释性。首先，设计了一个新的协同前件层和一个改进的后件层，以提升模糊模型在多步预测中的可解释性。其次，设计了一个新的变换层，以解决高维输入可能导致的规则触发强度消失问题。第三，提出了一个新的链接层，用于在多步预测之间建立时间关联。此外，开发了一种两阶段自组织机制来自动生成模糊规则：第一阶段用于从零开始创建规则库并进行初始优化，第二阶段则用于微调所有网络参数。最后，在混沌时间序列和微电网时间序列预测问题上进行了多种仿真实验，结果证明了所提方法在预测精度、不确定性处理和模型可解释性方面的优越性。