A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme.

中文翻译：

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深度学习循环 3 型模糊系统：可再生能源建模/预测的应用

提出了一种具有非线性后件部分的深度学习循环 3 型 (RT3) 模糊逻辑系统 (FLS)，用于可再生能源建模和预测。除了规则参数之外，还优化了水平切片和隶属函数（MF）参数的值。保证了建议学习方案的稳定性。所提出的方法适用于太阳能电池板和风力涡轮机的建模。通过使用实验设置和生成的真实数据集，显示了建议方法的适用性。与对流 FLS 的比较证明了所建议方案的优越性。