Global Journal of Technology and Optimization

College of Computer Science and Engineering, King Fahd University of petroleum and Minerals, Dhahran, Saudi Arabia

Publications

• Research Article   
  Influence of Principal Component Analysis as a Data Conditioning Approach for Training Multilayer Feedforward Neural Networks with Exact Form of Levenberg-Marquardt Algorithm
  Author(s): Najam Ul Qadir*, Md. Rafiul Hassan and Khalid Akhtar
  
  Artificial Neural Networks (ANNs) have generally been observed to learn with a relatively higher rate of convergence resulting in an improved training performance if the input variables are preprocessed before being used to train the network. The  foremost objectives  of  data  preprocessing  include  size  reduction  of  the  input  space,  smoother  relationship,  data  normalization,  noise  reduction,  and  feature  extraction. The most commonly used technique for input space reduction is Principal Component Analysis (PCA) while two of the most commonly used data normalization approaches include the min-max normalization or rescaling, and the z-score normalization also known as standardization. However, the selection of the most appropriate preprocessing method for a given dataset i.. Read More»
  

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