A Self-Adaptive Quantum Radial Basis Function Network for Classification Applications

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/6876

Title:	A Self-Adaptive Quantum Radial Basis Function Network for Classification Applications
Authors:	林正堅
Contributors:	資訊工程系
Date:	2011-12
Issue Date:	2017-12-17 13:15:38 (UTC+8)
Abstract:	In this paper, a self-adaptive quantum radial basis function network ((QRBF- N) is proposed for classification applications. The QRBFN model is with three layers, while the hidden layer contains quantum function neurons ((QFNs), equipped with multilevel activation functions. Each QFN is composed of the sum of sigmoid functions shifted by some specifid 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 backpropagation algorithm is used to tune the adjustable parameters. Simulation results on the three well-known benchmarking classification applications show that the proposed model outperforms to other relative approaches, in term of higher classification accuracies.
Relation:	International Journal of Innovative Computing, Information and Control
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

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