Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition

TL;DR

This study compares 12 backpropagation training algorithms for neural networks to identify the most effective one in recognizing data patterns, finding trainlm with alpha 5% to be the most suitable with 87.5% accuracy.

Contribution

It evaluates and identifies the most effective backpropagation training algorithm for pattern recognition in neural networks.

Findings

trainlm with alpha 5% achieved 87.5% accuracy

trainlm outperformed other algorithms in pattern recognition

most suitable algorithm identified with high significance

Abstract

There are several training algorithms for backpropagation method in neural network. Not all of these algorithms have the same accuracy level demonstrated through the percentage level of suitability in recognizing patterns in the data. In this research tested 12 training algorithms specifically in recognize data patterns of test validity. The basic network parameters used are the maximum allowable epoch = 1000, target error = 10-3, and learning rate = 0.05. Of the twelve training algorithms each performed 20 times looping. The test results obtained that the percentage rate of the great match is trainlm algorithm with alpha 5% have adequate levels of suitability of 87.5% at the level of significance of 0.000. This means the most appropriate training algorithm in recognizing the the data pattern of test validity is the trainlm algorithm.