International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
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.
How to cite this article:
P. JenoPaul and M. SreeDevi. Parameter Based Kalman Filter Training in Neural Network.
DOI: https://doi.org/10.36478/ijscomp.2013.352.355
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2013.352.355