Behavioral modeling of the Integrated Intelligent Classification Engine

摘要

Adaptive logic networks (ALNs) use an evolutionary mechanism to grow neural networks to have success as classifiers. As the tree in the ALN learns which branches are not providing any additional information, it evolves by deleting those branches. Herein, the rules for designing intelligent engines based on ALN classifiers are investigated. The behavior of a classification engine in different states is modeled to achieve a fast efficient design.