Abstract
This paper introduces a novel neuro-fuzzy inference system denoted as ldquoMUFIS: a neuro-fuzzy inference system using multiple types of fuzzy rulesrdquo, for allowing multiple types of fuzzy rules to be used together to achieve a better performance. At each data point, the output of MUFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from multi-type fuzzy rules. It is demonstrated that MUFIS can effectively implement prediction and function approximation. We evaluate its performance on two case studies - a benchmark time-series prediction problem - Mackey Glass, and a real life medical prediction problem - glomerular filtration rate prediction.
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