Performance Evaluation of Feed Forward Neural Network for Image Classification

Hadaate Ullah, M.A. Kiber, A.H.M. A. Huq, M.A.S. Bhuiyan

Abstract


Artificial Neural Networks (ANNs) are one of the most comprehensive tools for  classification. In this study, the performance of Feed Forward Neural Network (FFNN) with back-propagation algorithm is used to find out the appropriate activation function in the hidden layer using MATLAB 2013a. Random data has been generated and fetched to FFNN for testing the classification performance of this network. From the values of MSE, response graph and regression coefficients, it is clear that Tan sigmoid activation function is the best choice for the image classification. The FFNN with this activation function is better for any classification purpose of different applications such as aerospace, automotive, materials, manufacturing, petroleum, robotics, communication etc because to perform the classification the network designer  have to choose an activation function.

Keywords


Activation Function; ANN; Back-Propagation Algorithm; FFNN; Regression

Full Text:

PDF


Copyright (c) 2018 Journal of Science and Technology

ISSN : 2229-8460

e-ISSN : 2600-7924

jst@uthm.edu.my

Creative Commons License
This OJS site and its metadata are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.