Open AccessOptimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application
Author(s) -
Ahamad Zaki Mohamed Noor,
Muhammad Hafidz Fazli Md Fauadi,
Fairul Azni Jafar,
Muhamad Husaini Abu Bakar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2013.078219
Subject(s) - overfitting , artificial neural network , computer science , identification (biology) , pattern recognition (psychology) , value (mathematics) , artificial intelligence , biological neuron model , sample (material) , linear regression , regression , mathematics , statistics , machine learning , chemistry , botany , chromatography , biology
There were 50 data sample obtained from industries in Malaysia that practice sustainable manufacturing. Input file is presented in matrix 4x50 and 1x50 matrix as target file. However, there is no suitable number of hidden neuron that can be applied for the neural network model with 4 inputs and 1 output. An experiment has been done to identify the suitable hidden neuron through the observation of values from MSE and Regression. The hidden neuron must be no overfitting. The same goes for output and targets value must have close or linear relationship. The sample of tested hidden neuron is from 5 to 40 hidden neurons. The final answer obtained after look into Mean Square Error (MSE) values, Regression values and plots is hidden neuron 29. Hidden neuron 29 shows positive result in all criteria and should be implemented for this type of neural network model.