Comparative Performance Analysis of Neural Network Base Training Algorithm and Neuro-Fuzzy System with SOM for the Purpose of Prediction of the Features of Superconductors

Abstract

In this paper, the Neural Network based training algorithm has been discussed briefly. Correlation of coefficient (Training, Validation, Testing) using the Levenberg-Marquardt algorithm for superconductivity has been observed graphically. The same operation has been performed applying Bayesian Regularization algorithm and Scaled Conjugate Gradient algorithm. Mean Square Error and Regression has been calculated according to training, validation and testing using Bayesian Regularization, Scaled Conjugate Gradient and Levenberg-Marquardt algorithm for superconductor dataset. The target variable is the critical temperature of the superconductor. The regression value of Scaled Conjugate Gradient, Bayesian Regularization, Levenberg-Marquardt algorithm for superconductor dataset is 0.809214,0,0.854644, respectively which concludes that the Levenberg-Marquardt algorithm provides comparatively larger regression (R) value among them in validation state. Error histogram with 20 bins has been explained visually with simulation Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient algorithm. Neuro-Fuzzy system structure and Self-Organizing Maps (SOM) has also been implemented in this paper which provides the supremacy of the proposed work. The main benefit of SOM is that it is a useful multivariate visualization technique that permits the multidimensional data to be exposed as a 2-dimensional map.