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Neuro-Fuzzy Logic Model for Evaluating Water Content of Sandy Soils

机译:沙质土壤含水量的神经模糊逻辑模型

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In this study, the time-domain reflectometry (TDR) technique was utilized for generating an incident pulse signal of several harmonics of multiples of the fundamental frequency. The oscilloscope then captured reflected signals that were analyzed in the frequency domain by using Fourier transforms. The system response was characterized by its normalized spectral magnitude and phase angle. Experimental results indicated that the system is sensitive to water content and ion concentration. For water content prediction, a neuro-fuzzy logic model was developed. The changes in spectral magnitude and phase angle were considered as specific signatures for different soil conditions and were utilized in the training of the neuro-fuzzy logic model. The model was calibrated and used to predict other sets of data. The results indicated that the model is capable of predicting the water content of the tested soils.
机译:在这项研究中,时域反射法(TDR)技术用于产生入射脉冲信号,该入射脉冲信号具有基频倍数的多个谐波。然后,示波器捕获傅立叶变换在频域中分析的反射信号。系统响应的特征在于其归一化的频谱幅度和相位角。实验结果表明,该系统对水含量和离子浓度敏感。对于含水量预测,开发了神经模糊逻辑模型。光谱幅度和相角的变化被认为是不同土壤条件的特定特征,并被用于神经模糊逻辑模型的训练中。该模型已校准,可用于预测其他数据集。结果表明该模型能够预测被测土壤的含水量。

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