A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation

DOI10.1016/J.NEUNET.2013.06.004zbMath1296.68148DBLPjournals/nn/VukovicM13OpenAlexW2047950320WikidataQ43863814 ScholiaQ43863814MaRDI QIDQ461158

Najdan Vuković, Zoran Miljković

Publication date: 10 October 2014

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.06.004

zbMATH Keywords

extended Kalman filterfeedforward neural networkshyper basis functionneuron significancesequential learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

A novel sequential approximate optimization approach using data mining for engineering design optimization ⋮ Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise ⋮ Complete stability of delayed recurrent neural networks with Gaussian activation functions

Uses Software

• UCI-ml

Cites Work

• Unnamed Item
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• Generalized multiscale radial basis function networks
• An efficient self-organizing RBF neural network for water quality prediction
• Using radial basis function networks for function approximation and classification
• Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach
• Stochastic processes and filtering theory
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks
• Improved RAN sequential prediction using orthogonal techniques

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