Improved RAN sequential prediction using orthogonal techniques

  title={Improved RAN sequential prediction using orthogonal techniques},
  author={Mois{\'e}s Salmer{\'o}n and Julio Ortega and Carlos Garc{\'i}a Puntonet and Alberto Prieto},
A new learning strategy for time-series prediction using radial basis function (RBF) networks is introduced. Its potential is examined in the particular case of the resource allocating network model, although the same ideas could apply to extend any other procedure. In the early stages of learning, addition of successive new groups of RBFs provides an increased rate of convergence. At the same time, the optimum lag structure is determined using orthogonal techniques such as QR factorization and… CONTINUE READING


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Publications referenced by this paper.
Showing 1-10 of 20 references

Matrix computations (3. ed.)

View 5 Excerpts
Highly Influenced

A Resource-Allocating Network for Function Interpolation

Neural Computation • 1991
View 16 Excerpts
Highly Influenced


R. Rosipal, M. Koska
Farkas\ , Prediction of chaotic time-series with a resource-allocating RBF Network, Neural Process. Lett. 7 • 1998
View 2 Excerpts

Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm

L. Yingwei, N. Sundararajan, P. Saratchandran
IEEE Trans. Neural Networks 9 (2) • 1998
View 2 Excerpts

A sequential learning scheme for function interpolation using minimal radial basis function networks

L. Yingwei, N. Sundararajan, P. Saratchandran
Neural Comput. 9 • 1997
View 1 Excerpt

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