A SELF-ADAPTIVE QUANTUM RADIAL BASIS FUNCTION NETWORK FOR CLASSIFICATION APPLICATIONS

被引：0

作者：

Lin, Cheng-Jian ^{[1
]}

Peng, Chun-Cheng ^{[1
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 411, Taiwan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 12期

关键词：

Classification; Quantum function; Radial basis function; Self-clustering method; NEURAL-NETWORKS; DESIGN;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a self-adaptive quantum radial basis function network (QRBFN) is proposed for classification applications. The QRBFN model is with three layers, while the hidden layer contains quantum function nenurons (QENs), equipped with multilevel activation functions. Each QFN is composed of the sum of sigmoid functions shifted by sonic specific quantum intervals. A self-adaptive learning algorithm consisting of the self-clustering algorithm (SCA) and the backpropagation algorithm is proposed. The proposed SCA method is a fast, one-pass approach for a dynamic estimation of the number of clusters in the given input data space. The back-propagation algorithm is used to tune the adjustable parameters. Simulation results on the three well-known bench-marking classification applications show that lite proposed model outperforms to other relative approaches, in term of higher classification accuracies.

引用

页码：6621 / 6634

页数：14

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