[1]MENG Xi,QIAO Junfei,LI Wenjing.Construction of RBF neural networks via fast density clustering[J].CAAI Transactions on Intelligent Systems,2018,13(3):331-338.[doi:10.11992/tis.201702014]
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Construction of RBF neural networks via fast density clustering

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 331-338 Column: 学术论文—人工智能基础 Public date: 2018-05-05
Title:
Construction of RBF neural networks via fast density clustering
Author(s):
MENG Xi1; 2;  QIAO Junfei1; 2;  LI Wenjing1; 2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
Keywords:
RBF neural networks; fast density clustering; structure design; neuron activity; second-order training; generalization performance; function approximation; system identification
CLC:
TP273
DOI:
10.11992/tis.201702014
Abstract:
To design a hidden layer structure in radial-basis-function (RBF) neural networks, a novel algorithm based on fast density clustering is proposed. The algorithm searches for the point with the highest density and then uses it as the neuron of the hidden layer, thereby ascertaining the number of neurons in the hidden layer and the initial parameters. Moreover, the activity of each hidden neuron is ensured by introducing the Gaussian function. An improved second-order algorithm is used to train the designed network, increasing the training speed and improving the generalization performance. In addition, two benchmark simulations-the typical nonlinear function approximation and the nonlinear dynamic system identification experiment -are used to test the effectiveness of the proposed RBF neural network. The results suggest that the proposed RBF neural network based on fast density clustering offers improved generalization performance, has a compact structure, and requires shorter training time.
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