| This paper proposes a new method to reduce training time for neural nets used as function approximators. This method relies on a geometrical control of Multilayer Perceptrons (MLP). A geometrical initializa-tion gives rst better starting points for the learning process. A geometrical parametriza-tion achieves then a more stable convergence. During the learning process, a dynamic geometrical c...

The application of two general-purpose Pattern Recognition techniques (a Syntactic approach, Error Correcting Grammatical Inference, and a Geometric approach, Multilayer Percptron) to unidimensionally coded human chromosome recognition is considered in this paper. The results obtained with both techniques are comparable or better than previous results obtained by other authors with the very sam...

This paper focuses on the development of a hybrid phenomenological/inductive model to improve the current physical setup force model on a five stand industrial hot strip finishing mill. We approached the problem from two directions. In the first approach, the starting point was the output of the current setup force model. A feedforward multilayer perceptron (MLP) model was then used to estimate...

In this paper, a new Transform Domain implementation of the well known Multilayer Perceptron Neural Network is presented. With the Transform Domain implementation, the input of the Neural Network can be represented in a more compact manner and the elements of the input vector become uncorrelated. The new Transform Domain Multilayer Perceptron (TDMLP) Neural Network is applied for the problem of...

This paper proposes a new method to reduce training time for neural nets used as function approximators. This method relies on a geometrical control of Multilayer Perceptrons (MLP). A geometrical initialization gives first better starting points for the learning process. A geometrical parametrization achieves then a more stable convergence. During the learning process, a dynamic geometrical con...

Change point estimation in the area of statistical process control has received considerable attentions in the recent decades because it helps process engineer to identify and remove assignable causes as quickly as possible. On the other hand, improving in measurement systems and data storage, lead to taking observations very close to each other in time and as a result increasing autocorrelatio...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

This work introduces direction of arrival (DOA) estimation considering the sparsity prior in low signal to noise ratio (SNR) using deep learning (DL). The representation can be prompted realize super-resolution DOA spatial spectrum. undersampled noisy linear measurements spectrum, which is formulated by columns array covariance matrix, given as input neural network for estimation.The convolutio...

Multilayer feed-forward networks, or multilayer perceptrons (MLPs) have one or several " hidden " layers of nodes. This implies that they have two or more layers of weights. The limitations of simple perceptrons do not apply to MLPs. In fact, as we will see later, a network with just one hidden layer can represent any Boolean function (including the XOR which is, as we saw, not linearly separab...