A software engineering approach to develop adaptive RBF neural networks
Design and application of hybrid intelligent systems
Adaptive fuzzy control systems with dynamic structure
International Journal of Systems Science
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Remote sensing image fusion based on adaptive RBF neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Improved clustering and anisotropic gradient descent algorithm for compact RBF network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An application of pattern recognition based on optimized RBF-DDA neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
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In radial basis function (RBF) networks, placement of centers is said to have a significant effect on the performance of the network. Supervised learning of center locations in some applications show that they are superior to the networks whose centers are located using unsupervised methods. But such networks can take the same training time as that of sigmoid networks. The increased time needed for supervised learning offsets the training time of regular RBF networks. One way to overcome this may be to train the network with a set of centers selected by unsupervised methods and then to fine tune the locations of centers. This can be done by first evaluating whether moving the centers would decrease the error and then, depending on the required level of accuracy, changing the center locations. This paper provides new results on bounds for the gradient and Hessian of the error considered first as a function of the independent set of parameters, namely the centers, widths, and weights; and then as a function of centers and widths where the linear weights are now functions of the basis function parameters for networks of fixed size. Moreover, bounds for the Hessian are also provided along a line beginning at the initial set of parameters. Using these bounds, it is possible to estimate how much one can reduce the error by changing the centers. Further to that, a step size can be specified to achieve a guaranteed, amount of reduction in error.