Analytical Observation for classification of Multilayer Neuron Models using different datasets
DOI:
https://doi.org/10.26438/ijcse/v6i5.915Keywords:
Multilayer Neuron, Classification, analysis, ClassAbstract
In this paper, Multilayer Neuron model is used for classification of nonlinear problems. This conventional neuron model, is been taken for the analysis of while using different data sets. It is found, the Multilayer Neuron model showing its varying efficiency according to pattern of dataset. For analysis of model, various parameters of Artificial Neural Network like numbers of hidden neuron, number of attributes, learning rate, correlation coefficient, numbers of iteration, time elapse in training, mean square error etc. are being taken. After the analytical observation considering above various mentioned parameters, it is observed that there is no thump rule on behalf we can say that Multilayer Neuron Model follow the particular rule. The learning of model depends on the pattern of the dataset and the quality of data.
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