Supervised learning in CINets

Paul Bruhn, Jeffrey Weinschenk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Continuous Inference Networks (CINets), a form of multilayer fuzzy value networks, allow computation with fuzzy values in concise structures, are capable of universal function approximation, and are readily interpretable through natural language, aiding maintenance, modification, collaboration, and knowledge sharing. However CINets have been reliant on Subject Matter Expertise (SME) and manual tuning to realize optimal performance, limiting their applicability. With ONR support[i], ARL has developed a supervised learning process for CINets, capable of designing a CINet structure, and of optimizing an existing CINet structure. The CINet supervised learning process allows the automated development of data fusion, classification, and pattern recognition structures that are interpretable, modifiable, and concise. Performance of CINets developed with the supervised learning process is compared to that of Artificial Neural Network (ANNs), fuzzy logic rule set, and Bayesian network approaches.

Original languageEnglish (US)
Title of host publicationFusion 2011 - 14th International Conference on Information Fusion
StatePublished - 2011
Event14th International Conference on Information Fusion, Fusion 2011 - Chicago, IL, United States
Duration: Jul 5 2011Jul 8 2011

Publication series

NameFusion 2011 - 14th International Conference on Information Fusion

Other

Other14th International Conference on Information Fusion, Fusion 2011
Country/TerritoryUnited States
CityChicago, IL
Period7/5/117/8/11

All Science Journal Classification (ASJC) codes

  • Information Systems

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