Authors : P. JenoPaul and M. SreeDevi
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.
P. JenoPaul and M. SreeDevi, 2013. Parameter Based Kalman Filter Training in Neural Network. International Journal of Soft Computing, 8: 352-355.