Simulation of correlation activity pruning methods to enhance transparency of ANNs

The use of ANNs as predictors of natural phenomena is an important application but equally important is any resulting explanation of the heuristics a network uses to achieve this prediction. The novel methods of equation synthesis and correlated activation pruning (CAPing) are introduced and used to extract meaning from a trained ANN. Equation synthesis involves the incremental increase in the number of connections of the trained ANN used until satisfactory prediction is achieved. CAPing involves the identification of nodes that have similar effects on the desired output. Comparison of the inputs to these nodes can lead to useful dependency relationships. Several useful generalizations have been made in this project by using these methods. Generalizations have been made using ANNs, equation synthesis and CAPing. These techniques are applied to neural nets trained to simulate the pollutant/crop damage cause/effect relationship.