Data Quality in ANFIS Based Soft Sensors
Soft sensor are used to infer the critical process variables that are otherwise difficult, if not impossible, to measure in broad range of engineering fields. Adaptive Neuro-Fuzzy Inference System (ANFIS) has been employed to develop successful ANFIS based inferential model that represents the dynamics of the targeted system. In addition to the structure of the model, the quality of the training as well as of the testing data also plays a crucial role in determining the performance of the soft sensor. This paper investigates the impact of data quality on the performance of an ANFIS based inferential model that is designed to estimate the average air temperature in distributed heating systems. The results of the two experiments are reported. The results show that the performance of ANFIS based sensor models is sensitive to the quality of data. The paper also discusses how to reduce the sensitivity by an improved mathematical algorithm.
KeywordsRoot Mean Square Error Predictive Accuracy Data Item Fuzzy Inference System ANFIS Model
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