  The concept of a recently proposed Forward-Forward learning algorithm for
fully connected artificial neural networks is applied to a single multi output
perceptron for classification. The parameters of the system are trained with
respect to increased (decreased) "goodness" for correctly (incorrectly)
labelled input samples. Basic numerical tests demonstrate that the trained
perceptron effectively deals with data sets that have non-linear decision
boundaries. Moreover, the overall performance is comparable to more complex
neural networks with hidden layers. The benefit of the approach presented here
is that it only involves a single matrix multiplication.
