Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition

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Original languageEnglish
Pages (from-to)1623-1629
Journal / PublicationIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - Nov 2006
Externally publishedYes


In this letter, we attempt to quantify the significance of increasing the number of neurons in the hidden layer of a feedforward neural network architecture using the singular value decomposition (SVD). Through this, we extend some well-known properties of the SVD in evaluating the generalizability of single hidden layer feedforward networks (SLFNs) with respect to the number of hidden layer neurons. The generalization capability of the SLFN is measured by the degree of linear independency of the patterns in hidden layer space, which can be indirectly quantified from the singular values obtained from the SVD, in a postlearning step. A pruning/growing technique based on these singular values is then used to estimate the necessary number of neurons in the hidden layer. More importantly, we describe in detail properties of the SVD in determining the structure of a neural network particularly with respect to the robustness of the selected model.