Monadic Second-Order Fuzzy Logic expert system

Reasoning with uncertain information is a problem of key importance when dealing with real knowledge. This paper proposes Monadic Second-Order (MSO) Fuzzy Logic and Fuzzy Cross Filter for expressing complex relationship between rules and formalizing knowledge. MSO Fuzzy Logic combines statistical-based fuzzy theory and second order logic to design a novel frame of expert system for improving accuracy and robustness in uncertain data environment, and has more powerful express ability. Fuzzy Cross Filter Eliminates the system bias of rules in the initialization step of expert system, and builds the formalized knowledge network according to MSO Fuzzy Logic. The experiment results show that the approach can improve the accuracy by about 5.1% and increase the robustness by about 11.8% comparing with other systems based on J48, Bayes Net or Bagging on the waveform data set which includes 5000 instances.