Nps For Knowledge-engineering

Introduction:-Fuzzy production systems use fuzzy logic inference techniques, with the use of multilayer perceptrons and the backpropagation learning algorithm, and with some other inference techniques. The main idea of realizing fuzzy production systems as an NPS is that activation values of nodes in the WM contextually represent membership degrees when dealing with fuzzy variables.

ExampleThe Bank Loan fuzzy rules are implemented in an NPS, as shown in figure above. New input values (e.g., Score = 190, Ratio = 0.4, Credit = 3) must be fuzzified first and then used as activation values for the neurons in the WM. After a run of the NPS, new facts about the decision are inferred. The latter can be applied over the Decision Approve and Decision Disapprove fuzzy membership functions, and the final value for the Decision variable obtained after deffuzification. A realization of a fuzzy production system using an NPS assumes that fuzzification and defuzzification are done as preprocessing and post processing.