Hebb_FixAct

  This rule is necessary in order to do 'one-step-recall' simulations. For more informations see [Ama89]. For the calculation the bias following facts are assumed:

• The implemented net is an autoassociative net
  The neurons of an autoassociative net have to be input and output neurons at the same time. A Hopfield network would be an example for such a net.

• Fixed number of 1s
  The patterns which are to be saved have a fixed number of 1s.

The parameter h1 and h2 are required. Where h1 is the number of ones per pattern and h2 is the probable degree of distortion in percent. The parameters have to be inserted in field1 and field2. This initialization function should be used only in connection with the Hopfield_Fixed_Act update function. As mentioned in section the Hebb_FixAct algorithm is a learning algorithm.