Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

作者：Gill Amaninder Singh; Summers Joshua D; Turner Cameron J

来源：Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM , 2017, 31(4): 550-566.

DOI：10.1017/S0890060417000543

• Artificial Neural Networks
• Benchmarking
• Complexity
• Function Structures
• Functional Composition
• Graph Complexity Connectivity Method
• Information Content
• Pruning

Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

• 出版日期2017-11