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Adaptive Neuro-Fuzzy Inference System for Predicting Compressive Strength of Fibres Self Compacting Concrete

机译:用于预测纤维抗压强度的自适应神经模糊推理系统

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This research focused on the applicability of Adaptive Network-Based Fuzzy Inference System (ANFIS) for predict the compressive strength of fibers self compacting concrete. An ANFIS model combines the benefit of ANN and fuzzy logic. The data developed experimentally for fibers self compacting concrete and the data sets of a total 99 concrete samples were used in this work. In this paper research is computational based for prediction of concrete compressive strength. A model was developed using ANFIS with five input nodes as w/p ratio, course aggregate, fine aggregate, fiber and superplastizers. In this model Feed-forward three-layer back-propagation neural networks with 10 hidden nodes were examined using learning algorithm. ANFIS model proposed analytically that gives more compatible results. Hence, the model is adopted to predict the strength of fibrous self compacting concrete.
机译:本研究专注于适应性基于网络的模糊推理系统(ANFIS)的适用性来预测纤维自压缩混凝土的抗压强度。 ANFIS模型结合了ANN和模糊逻辑的好处。在实验上开发的数据用于纤维自压实混凝土和总共99个混凝土样品的数据集。在本文中,研究是基于混凝土抗压强度的预测的计算。使用具有五个输入节点的ANFIS开发了一种模型,如W / P比,课程骨料,精细骨料,光纤和超级塑料。在该模型中,使用学习算法检查具有10个隐藏节点的三层背传播神经网络。 ANFIS模型在分析上提出,提供更兼容的结果。因此,采用该模型来预测纤维自压力混凝土的强度。

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