Three learning phases for radial-basis-function networks

By Friedhelm Schwenker, Hans A. Kestler, Günther Palm. 2001

518 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “RBF networks have many uses, including function approximation, time series prediction, classiﬁcation, and system control [36].”
Global optimization issues in deep network regression: an overview (2018, methodology): “Such a procedure has been proposed in [124].”
Machine learning methods for turbulence modeling in subsonic flows around airfoils (2018, methodology): “Once the sample group is determined, the parameters of center and width in each neuron can be obtained by various algorithms like gradient descent (GD), orthogonal least square and recurrent least square and so on [33-35].”
Applications of artificial neural networks for thermal analysis of heat exchangers – A review (2015, background): “RBFN has a single hidden layer, whereas a MLFFN may have more than one hidden layer, which needs to be optimized [180].”
Unsupervised Large Graph Embedding (2017, background): “…(Ng, Jordan, and Weiss 2001), semi-supervised learning (Zhou et al. 2003; Zhu 2008), dimensionality reduction (Nie et al. 2011), RBF networks (Schwenker, Kestler, and Palm 2001), etc. Anchor-based Similarity Matrix Let U〈i〉 denote the set of k-nearest anchors for the i-th sample, where U ∈…”
Neural Network Ensembles in Reinforcement Learning (2015, methodology): “Several RL methods have been combined with function approximation techniques, mainly linear or generalized linear models [2] such as radial basis function networks [12].”