Optimal Number of Hidden Neuron Identification for Sustainable Manufacturing Application
Ahamad Zaki Mohamed Noor1, Muhammad Hafidz Fazli Md Fauadi2, Fairul Azni Jafar3, Muhamad Husaini Abu Bakar4
1Ahamad Zaki Mohamed Noor, Manufacturing Section, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim, Malaysia.
2Muhammad Hafidz Fazli Md Fauadi, Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia, Melaka, Malaysia.
3Fairul Azni Jafar, Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia, Melaka, Malaysia.
4Muhamad Husaini Abu Bakar, Manufacturing Section, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim, Malaysia.
Manuscript received on 03 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 2447-2453 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2013078219/19©BEIESP | DOI: 10.35940/ijrte.B2013.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There were 50 data sample obtained from industries in Malaysia that practice sustainable manufacturing. Input file is presented in matrix 4×50 and 1×50 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.
Index Terms: Hidden Neuron, Mean Square Error, Neural Network, Regression, Sustainable Manufacturing.
Scope of the Article: Sustainable Structures