Fuzzy multi-layer perceptron, inferencing and rule generation

Author

Mitra, Sushmita ; Pal, Sankar K.

Author_Institution

Machine Intelligence Unit, Indian Stat. Inst., Calcutta, India

Volume

6

Issue

1

fYear

1995

fDate

1/1/1995 12:00:00 AM

Firstpage

51

Lastpage

63

Abstract

A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the more important input feature information, if and when required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The magnitudes of the connection weights 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 speech recognition problem, on some medical data and on artificially generated intractable (linearly nonseparable) pattern classes

Keywords

fuzzy neural nets; inference mechanisms; multilayer perceptrons; pattern recognition; artificially generated intractable pattern classes; connectionist expert system model; fuzzy inferencing; fuzzy multilayer perceptron; fuzzy rule generation; inferred decision; linearly nonseparable pattern classes; medical data; multilayer perceptron; output class membership value; speech recognition; trained neural network; Artificial neural networks; Expert systems; Fuzzy sets; Fuzzy systems; Hybrid intelligent systems; Inference algorithms; Medical tests; Multilayer perceptrons; Neural networks; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.363450

Filename

363450