Blurb::
Maximum number of hidden layer nodes
Description::
Limits the maximum number of hidden layer nodes in the neural network
model.  The default is to use one less node than the number of
available training data points yielding a fully-determined linear
least squares problem.  However, reducing the number of nodes can help
reduce overfitting and more importantly, can drastically reduce
surrogate construction time when building from a large data set.
(Historically, Dakota limited the number of nodes to 100.)

The keyword ``max_nodes`` provides an upper bound.  Dakota's orthogonal
matching pursuit algorithm may further reduce the effective number of
nodes in the final model to achieve better generalization to unseen
points.
Topics::
surrogate_models
Examples::

Theory::

Faq::

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