This paper integrates the evidential reasoning methodology with the parallel distributed learning paradigm of artificial neural networks (ANN). As such, this work presents an algorithm for the detection and, if possible, subsequent correction of the errors in the neuron responses in the output layer of the multiple adaptive linear element (MADALINE) ANN. A geometrical perspective of the MADALINE ANN processing methodology is provided. This perspective is then used to formulate a statistical specification to identify and quantify the sources of uncertainties in the MADALINE processing methodology. A new algorithm, EMRII, is then developed as an extension to the original MRII (MADELINE rule II) algorithm, to formulate support and plausibility measures based on the statistical specification. The support and plausibility measures, thus formulated, are indicative of the degree of confidence of the ANN, in regards to the correctness of its outputs. Based on the support measure, a scheme utilizing two thresholds is proposed to facilitate the interpretation of the support values for error prediction in the ANN responses. Finally, simulation results for the application of the EMRII algorithm in the prediction of erroneous responses in an example problem is presented. These simulation results highlight the error detection capabilities of the EMRII algorithm. >
['Chaitanya Tumuluri', 'Pramod K. Varshney']
An evidential extension of the MRII training algorithm for detecting erroneous MADALINE responses