On the structure and initial parameter identification of Gaussian RBF networks.
We consider the efficient initialization of structure and parameters of generalized Gaussian radial basis function (RBF) networks using fuzzy decision trees generated by fuzzy ID3 like induction algorithms. The initialization scheme is based on the proposed functional equivalence property of fuzzy decision trees and generalized Gaussian RBF networks. The resulting RBF network is compact, easy to induce, comprehensible, and has acceptable classification accuracy with stochastic gradient descent learning algorithm.
Bhatt
Rajen B
RB
Control laboratories, II/214, Department of Electrical Engineering, Indian Institute of Technology-Delhi, Hauz Khas, New Delhi-110016, India. bhattrajen@ee.iitd.ernet.in
Gopal
M
M
D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D016011:Normal Distribution