This posting will not go into much detail, but this type of model is something like a logistic regression, where a linear model is calculated on the input variables, then passed through a squashing function (in this case the logistic curve). Such models are most often used to model binary outcomes, hence the dependent variable is normally composed of the values 0 and 1.

Single neurons with linear functions (with squashing functions or not) are only capable of separating classes that may be divided by a line (plane, hyperplane), yet they are often useful, either by themselves or in building more complex models.

Use

*help DeltaRule*for syntax and a simple example of its use.

Anyway, I thought readers might find this routine useful. It trains quickly and the code is straightforward (I think), making modification easy. Please write to let me know if you do anything interesting with it.

If you are already familiar with simple neural models like this one, here are the technical details:

Learning rule: incremental delta rule

Learning rate: constant

Transfer function: logistic

Exemplar presentation order: random, by training epoch

See also the Mar-15-2009 posting, Logistic Regression and the Dec-11-2010 posting, Linear Discriminant Analysis (LDA).