Title :
A self-adaptive quantum radial basis function network for classification applications
Author :
Lin, Cheng-Jian ; Chen, Cheng-Hung ; Lee, Chi-Yung
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Chaoyang Technol. Univ., Taichung, Taiwan
Abstract :
A self-adaptive quantum radial basis function network (QRBFN) is proposed for classification applications. The QRBFN model is a three-layer structure. The hidden layer of the QRBFN model contains quantum function neurons, which are multilevel activation functions. Each quantum function neuron is composed of the sum of sigmoid functions shifted by quantum intervals. A self-adaptive learning algorithm, which consists of the self-clustering algorithm (SCA) and the backpropagation algorithm, is proposed. The proposed the 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.
Keywords :
adaptive systems; backpropagation; pattern classification; radial basis function networks; backpropagation algorithm; classification; quantum function neuron; self-adaptive learning algorithm; self-adaptive quantum radial basis function network; self-clustering algorithm; sigmoid function; Backpropagation algorithms; Clustering algorithms; Educational institutions; Function approximation; Heuristic algorithms; Neural networks; Neurons; Partitioning algorithms; Radial basis function networks; Robust stability;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Conference_Location :
Budapest
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381202
