抽象的な

A Neroph Approach for Classifying E. coli Data

Christopher Ejiofor*, Okon and Emmanuel Uko

Classifications are used in handling classification problems, which usually existing when entities or objects need to assigned predefined groups or classes, perhaps based on attributes, parameters and values. This research paper provide a simplify description of neuroph classification using E. coli data. The data were structured into training and testing data. The neuroph neural network architecture caters for 34 neurons: twelve input neurons (12), seventeen (17) hidden neurons and five (5) output neurons. The training accommodated approximately 185 iterations with a cumulative error of 6.8544 and an average error of 0.0367. The Total Mean Square Error (TMSE) obtained from testing the trained data gave an approximate value of 2.7372. This minima error showed the optimality in training and classification using E. coli data.