Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A connectionist inferencing network, based on the fuzzy version of Kohonen's model already developed by the authors, is proposed. It is capable of handling uncertainty and/or impreciseness in the input representation provided in quantitative, linguistic and/or set forms. The output class membership value of an input pattern is inferred by the trained network. A measure of certainty expressing confidence m the decision is also defined. The model is capable of querying the user for the more important input feature information, if required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The connection ight magnitude of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the vol recognition problem and on two sets of artificially generated nonconvex pattern classes.
['Sushmita Mitra', 'Sankar K. Pal']
I am writing a research paper on a idea Fuzzy self-organization, inferencing, and rule generation
[{"from": "human", "value": "I am writing a research paper on a idea Fuzzy self-organization, inferencing, and rule generation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A connectionist inferencing network, based on the fuzzy version of Kohonen's model already developed by the authors, is proposed. It is capable of handling uncertainty and/or impreciseness in the input representation provided in quantitative, linguistic and/or set forms. The output class membership value of an input pattern is inferred by the trained network. A measure of certainty expressing confidence m the decision is also defined. The model is capable of querying the user for the more important input feature information, if required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The connection ight magnitude of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the vol recognition problem and on two sets of artificially generated nonconvex pattern classes."}]