The modeling of an intermediate temperature solid oxide fuel cell using an adaptive neuro-fuzzy inference system (ANFIS) has been presented. The results show that a well trained and well tested ANFIS model has the capability to predict the fuel cell performance under varying operational conditions depending on the availability of the data and can be used as an alternative to the physical models in the sense that the results can be produced in a fast and cost effective way. The performance of the model in regions where there is data deficiency has been discussed.
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