Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

Data mining techniques can be used to discover useful patterns by exploring and analyzing data and it’s feasible to synergistically combine machine learning tools to discover fuzzy classification rules. In this paper, an adaptive neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. Quantum clustering (QC) is an intuition from quantum mechanics which uses Schrödinger potential and time-consuming gradient descent method. The principle advantage and shortcoming of QC is analyzed and based on its shortcomings, an improved algorithm through a subtractive clustering method is proposed. Cluster centers represent a general model with essential characteristics of data which can be use as premise part of fuzzy rules. The experimental results revealed that proposed Anfis based on quantum subtractive clustering yielded good approximation and generalization capabilities and impressive decrease in the number of fuzzy rules and network output accuracy in comparison with traditional methods.