@article{MAKHILLIJSC20138321143,
    title = {Performance Comparison for MLP Networks Using Various Back Propagation Algorithms in Epileptic Seizure Detection},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {207-217},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.207.217},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.207.217},
    author = {K. and},
    keywords = {Epilepsy,independent component analysis,backpropagation algorithms,Gradient Descent algorithm (GD),Scaled Conjugate Gradient (SCG),One Step Secant (OSS),Powell-Beale Restarts (PBR),Gradient Descent with Adaptive (GDWA),Fletcher-Powell Conjugate Gradient (FPCG) and Levenberg Marquardt (LM) backpropagation,epoch,regression},
    abstract = {Epilepsy can be diagnosed using technologies like Electroencephalogram 
  (EEG), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), 
  etc. In this research, researchers inspect EEG signals for seizures. The seizure 
  is recognized with the support of Independent Component Analysis and the ascertained 
  signals are trained under supervision by making use of neural networks technique 
  namely backpropagation algorithm. In the proposal, performance of network is 
  evaluated using publicly available EEG dataset for various backpropagation training 
  functions such as Gradient Descent Algorithm (GD), Scaled Conjugate Gradient 
  (SCG), One Step Secant (OSS), Powell-Beale Restarts (PBR), Gradient Descent 
  with Adaptive (GDWA), Fletcher-Powell Conjugate Gradient (FPCG) and Levenberg 
  Marquardt (LM) Backpropagation are used here for the comparison technique. On 
  comparing the performance of these aforementioned algorithms, highest accuracy 
  with lowest mean square error was obtained for scaled conjugate gradient.}
    }