Neuro-fuzzy model for evaluating the performance of processes using transfer function

Abstract

In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model the periodic performance of some multi-input single-output (MISO) processes, namely: brewery operations (case study 1) and soap production (case study 2) processes. Two ANFIS models were developed to model the performance of the two processes under study. The results of the study show that for brewery operations, ANFIS model 2 with a correlation coefficient of 0.9972, as against 0.9956 for ANFIS model 1, had a better correlation than an equivalent MAMDANI fuzzy model. On the order hand, for soap production process, ANFIS model 1 had better correlation with an equivalent MAMDANI model. Generally, there is a general agreement among the models on the periodic performance of the processes. Thus, all the models show that for the brewery, the best performance was in the period 2010–2011 and the period 2008–2009 was the worst. Similarly, for the soap production process, the best performance was in 2011 and the worst in 2012. The results show that a combination of transfer function and ANFIS could be used effectively to model process performance.