Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

TL;DR

This paper introduces an adaptive neuro-fuzzy network utilizing quantum subtractive clustering, which improves rule reduction and accuracy in data classification tasks by combining quantum mechanics principles with clustering techniques.

Contribution

It develops a novel neuro-fuzzy network based on an improved quantum subtractive clustering algorithm, enhancing rule efficiency and model performance.

Findings

Achieved significant reduction in fuzzy rules.

Demonstrated improved approximation and generalization.

Outperformed traditional methods in accuracy.

Abstract

Data mining techniques can be used to discover useful patterns by exploring and analyzing data and it's feasible to synergitically 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 Schrodinger 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.