A self-organizing quantum neural fuzzy network and its applications

A self-organizing quantum neural fuzzy network and its applications

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Article ID: iaor20084103
Country: United Kingdom
Volume: 37
Issue: 8
Start Page Number: 839
End Page Number: 859
Publication Date: Dec 2006
Journal: Cybernetics and Systems
Authors: ,
Keywords: fuzzy sets
Abstract:

In this paper, a self-organizing quantum neural fuzzy network (QNFN) is proposed. The QNFN model is a four-layer structure. Layer 2 of the QNFN model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast one-pass algorithm for a dynamic estimation of the number of clusters in an input data space. The backpropagation algorithm is used to tune the adjustable parameters. Simulation results were conducted to show the performance and applicability of the proposed model.

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