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Convergent decomposition techniques for training RBF neural networks. (English) Zbl 0986.68109
Summary: We define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers. Then we define a decomposition algorithm in which the selection of the center locations is split into sequential minimizations with respect to each center, and we give a suitable criterion for choosing the centers that must be updated at each step. We prove the global convergence of the proposed algorithms and report the computational results obtained for a set of test problems.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
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[1] DOI: 10.1080/10556789908805730 · Zbl 0940.65070 · doi:10.1080/10556789908805730
[2] DOI: 10.1016/S0167-6377(99)00074-7 · Zbl 0955.90128 · doi:10.1016/S0167-6377(99)00074-7
[3] DOI: 10.1162/neco.1989.1.2.281 · doi:10.1162/neco.1989.1.2.281
[4] DOI: 10.1109/5.58326 · doi:10.1109/5.58326
[5] DOI: 10.1007/BF01584660 · Zbl 0258.90043 · doi:10.1007/BF01584660
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