@article{MAKHILLIJSC20138521162,
    title = {Parameter Based Kalman Filter Training in Neural Network},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {5},
    pages = {352-355},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.352.355},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.352.355},
    author = {P. and},
    keywords = {KF-kalman filtering,neural networks,NNs,brain,fault},
    abstract = {Neural Networks (NNs) have been employed in many applications 
  in recent years. A neural network is an interconnection of a number of artificial 
  neurons that simulate a biological brain system. It has the ability to approximate 
  nonlinear functions and can achieve higher degree of fault tolerance. NNs have 
  been successfully introduced into power electronics circuits where a NN replaced 
  a large and memory demanding look-up table to generate the switching angles. 
  The neural network controllers for engine idle speed and Air/Fuel (A/F) ratio 
  control produce signals that affect the operation of the engine while the neural 
  network models are used to describe various aspects of engine operation as a 
  function of measurable engine outputs. This study aims to study the behavior 
  of the parameter based kalman filtering in neural network.}
    }