  Incorporation of a new knowledge into neural networks with simultaneous
preservation of the previous one is known to be a nontrivial problem. This
problem becomes even more complex when new knowledge is contained not in new
training examples, but inside the parameters (connection weights) of another
neural network. Here we propose and test two methods allowing combining the
knowledge contained in separate networks. One method is based on a simple
operation of summation of weights of constituent neural networks. Another
method assumes incorporation of a new knowledge by modification of weights
nonessential for the preservation of already stored information. We show that
with these methods the knowledge from one network can be transferred into
another one non-iteratively without requiring training sessions. The fused
network operates efficiently, performing classification far better than a
chance level. The efficiency of the methods is quantified on several publicly
available data sets in classification tasks both for shallow and deep neural
networks.
