Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods

Jian Min Zhu; Peng Du; Ting Ting Fu

doi:10.4028/www.scientific.net/AMR.317-319.1529

Paper Titles

Road Adaptive Suspension Controller Based on Partial-State Feedback Gain Scheduling
p.1507

Application of Fuzzy Adaptive Kalman Filtering on GPS/INS Integrated Navigation System
p.1512

Reconfigurable Design and Implementation of Gravure Engraving Motion Controller Based on FPGA
p.1518

The Processing Way of Pipeline Vibration Signal Based on Wavelet Transform
p.1525

Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods
p.1529

Image Encryption Algorithm Based on 3D Arnold Cat and Logistic Map
p.1537

Lateral Control of Roll-to-Roll System Using Fuzzy Control Logic and Vision Sensor
p.1541

Development of Manufacturing Execution Management System for Workshops Based on IOT Technology
p.1547

Design of Electron Beam Polishing Equipment on Mould
p.1555

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319Research for RBF Neural Networks Modeling Accuracy...

Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods

Abstract:

The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.