Applying the Abductory Induction Mechanism (AIM) to the extrapolation of chaotic time series

The authors present research done to develop ontogenic neural networks. One commercially available product, considered an ontogenic neural network, is the Abductory Induction Mechanism (AIM) program from AbTech Corporation of Charlottesville, Virginia. The methodology will discard any inputs it finds having a low relevance to predicting the training output. The depth and complexity of the network is controlled by a user-set complexity penalty multiplier (CPM). Results are presented using AIM to predict the output of the Mackey-Glass equation as the generator of the chaotic time series. Comparisons are made based on the root mean square (RMS) error for an iterated prediction of 100 time steps beyond the training set. The effects of different CPM values were explored, and it was found that a CPM value of 4.8 gives the best predictive results with the least computational complexity