Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi–Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms⁻¹ airflow velocity.

中文翻译：

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评估用于 Echium amoenum Fisch 传质建模的多层感知器神经网络和自适应神经模糊推理系统。＆CA梅

利用多层感知器 (MLP) 前馈人工神经网络 (ANN) 和一阶 Takagi-Sugeno 型自适应神经模糊推理系统 (ANFIS) 对Echium amoenum的流化床干燥过程进行建模菲施。＆CA 梅。基于干燥温度、气流速度和处理时间计算水分比变化。通过评估神经元的数量（3 到 20 个）、激活函数和第二个隐藏层的添加来检查不同的 ANN 拓扑。使用网格划分方法检查 ANFIS 的不同数量（2 到 5）和隶属函数的形状。将根据统计指标评估的预测精度性能最佳的模型与最佳拟合薄层模型以及来自 40 °C、50 °C 和 60 °C 温度的实验案例的可用数据进行比较在 0.5、0.75 和 1 ms ^-1气流速度下。