Similar to the Backpropagation algorithm, but with the difference that the error is estimated according to the Hessian Matrix. This matrix gives information of several directions where to go in order to find the minimum of the error function, instead of the local minimum one that gives the backpropagation algorithm.
Published in Chapter:
Intelligent Radar Detectors
Raúl Vicen Bueno (University of Alcalá, Spain), Manuel Rosa Zurera (University of Alcalá, Spain), María Pilar Jarabo Amores (University of Alcalá, Spain), Roberto Gil Pita (University of Alcalá, Spain), and David de la Mata Moya (University of Alcalá, Spain)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch137
Abstract
The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.