Abstract
The classification of nonlinear problems, single Multiplicative Neuron model is used. Several different databases like Fisher’s Iris database, wiconsin Breast Cancer, Mammographic Mass and Pima Indian have selected for study. The measure of dispersion has been found of all datasets by measuring standard deviation and range of dataset. The classification results of all problems compared and comparative analysis has been made of all datasets, considering several elements of Neural Network such as number of epochs, cost function, MSE and misclassification rate. After comparing various performance parameters mainly misclassification rate with measures of dispersion, it is found that misclassification rate of multiplicative neuron model depends only patterns of datasets. Seeing the results of study, it can be said that classification rate does not depend on the measure of dispersion (Standard deviation and Range) of datasets.
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