Performance Evaluation of Feed Forward Neural Network for Image Classification
Hadaate Ullah, Md. Adnan Kiber, Aminul Huq, Mohammad Arif Sobhan Bhuiyan · Journal of Science and Technology · 2018
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.