Lazy Quantum clustering induced radial basis function networks (LQC-RBFN) with effective centers selection and radii determination

Abstract

The Radial Basis Function Networks (RBFN) model has been successfully applied to different application scenarios as a universal approximator because of its simple architecture and online training capability. The approximation capability of RRBFN is greatly dependent on determination of the centers and the radii of the radial basis functions (RBFs) in the networks structure. Statistics-based centers determination approaches like K-means fail to capture and preserve the training data structure. In this paper, a new unsupervised RBFN construction methodology called Lazy Quantum Clustering induced Radial Basis Function Networks (LQC-RBFN) is proposed. It inherits the advantage of data structure learning and shows high robustness towards data distribution of Quantum Clustering (QC). At the same time, the controlling parameter can be determined arbitrarily without the requirement of precise calibration, and the minima search is done only once for a specific training data set. The centers and radii are selected based on the potential function generated by quantum assimilation, and the networks structure is adaptively updated incorporating the centers information. A series of application studies are presented to verify the effectiveness of the proposed LQC-RBFN model.