Developing rules to classify surface ships and submarines based on noisy features extracted from underwater acoustic signals is difficult and time consuming; indeed, it is difficult to generate decision making rules for any complex system. The Operational Evaluation Modeling (OpEM) Simulation Tool Kit including Expert System Controller and Induction Program, discussed in this paper, greatly mitigates this problem for most complex system simulation projects such as airports, factories, or space defense systems. A theory for inductive learning of decision making rules in cybernetic systems is presented to provide a concise way-of thinking about inductive learning. An OpEM directed graph model is presented that describes operation of a sonar detection and classification system. An OpEM Pascal simulation of this system demonstrates how effective classification rules can be that are induced from simulation generated cases, even with noisy features present. The OpEM induction program is described and innovative features are discussed. Rules generated by the OpEM induction program are compared to rules obtained by Ross Quinlan's ID3 induction program. Performance of the OpEM induction program when noisy features are present is compared with ID3.
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