On the structure and initial parameter identification of Gaussian RBF networks
RB Bhatt, M Gopal - International journal of neural systems, 2004 - World Scientific
RB Bhatt, M Gopal
International journal of neural systems, 2004•World ScientificWe 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.
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.
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.
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