Chemical plant fault diagnosis through a hybrid symbolic-connectionist machine learning approach

摘要: A novel hybrid symbolic-connectionist approach to machine learning is introduced and applied fault diagnosis of a hydrocarbon chlorination plant. The algorithm addresses the knowledge acquisition problem by developing maintaining base through instance based inductive learning. performance system discussed in terms extracted from example cases its classification accuracy on test cases. Results indicate that promising alternative neural networks for complement expert systems.