CONTRIBUTED ARTICLE Logical Op ration Based Fuzzy MLP for Classification and Rule

Abstract-A jilzzy layered neural network/or classification and rule generation is proposed using logical neurons. II can handle uncertainty and/or impreciseness in the input as well as the output. Logical operators, namely, t norm T and t-conorm S involving And and Or neurons, are employed in place of the weighted sum and sigmoid functions. Various/uzzy implication operators are introduced to incorporate different amounts ofmutual interaction during the back propagation Q { errors. In case 0/partial inputs the model is capable of querying the user/or the more important/eature information, (f and when required. Justification for an inferred decision may be produced in rule/orm. The built-in And-Or structure of the network enables the generation Qf appropriate rules expressed as the disjunction Qf conjunctive clauses. The e.ffectiveness o.fthe model is tested on a speech recognition problem and on some art({tcially generated pattern sets