神经网络与模糊逻辑决策集成与双层神经网络在日露点温度模拟中的比较
Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature
摘要：
在本研究中，露点温度（DPT）的模拟使用数据驱动的方法。自适应神经模糊推理系统（ANFIS）被用作数据驱动的技术来预测这个参数在大不里士在东阿塞拜疆。各种输入模式，即T min，T max和T mean，用于训练架构，而DPT是模型的输出。研究结果表明，在一般情况下，ANFIS方法是能够识别数据模式具有较高的准确度。然而，该方法表明，处理时间和计算机资源可能会大大增加，通过添加额外的功能。根据结果，如果包含新功能，迭代次数和计算资源可能会发生显着变化。因此，必须在方法框架内优化调优参数。研究结果表明，数据驱动技术（机器学习方法）和观察到的数据之间的结果高度一致。使用这个预测工具包，DPT可以完全根据大不里士的温度分布进行预测。这种建模是非常有前途的预测DPT在不同的网站。此外，本研究亦将双层神经网络（BNN）与自适应模糊推理系统（ANFIS）在不同尺度下进行比较。虽然ANFIS模型对于几乎所有数量的隶属函数都非常稳定，但BNN模型对该比例因子高度敏感以预测DPT。
英文摘要：
In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a data-driven technique to forecast this parameter at Tabriz in East Azerbaijan. Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture whilst DPT is the model's output. The findings indicate that, in general, ANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the data-driven technique (machine learning method) and the observed data. Using this prediction toolkit, DPT can be adequately forecasted solely based on the temperature distribution of Tabriz. This kind of modeling is extremely promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales. Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
