Patients face a multitude of diseases, trauma, and related medical problems that are difficult to diagnose and have large treatment and diagnostic direct costs, including pulmonary embolism (PE), which has mortality rates as high as 10%. Advanced decision-making tools, such as nonparametric neural networks (NN), may improve diagnostic capabilities for these problematic medical conditions. The r...

This paper examines the application of neural networks (NN) to reliability-based structural optimization of largescale structural systems. The failure of the structural system is associated with the plastic collapse. The optimization part is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method incorporating the importanc...

The active magnetic bearing (A solution for all the technical problems of the since it ensures the total levitation of a eliminating any mechanical contact between stator. The goal of our work is to show the co a magnetic sustention, characterized by its using neural networks (NN). In this paper controller for a magnetic bearing under a control is presented. Keywords-active magnetic bearing; al...

Neural networks are a very successful machine learning technique. At present, deep (multi-layer) neural networks are the most successful among the known machine learning techniques. However, they still have some limitations, One of their main limitations is that their learning process still too slow. The major reason why learning in neural networks is slow is that neural networks are currently ...

Linear regression methods have been applied for decades and are well known and understood (Millionis, A.E. and T.D. Davies, 1994; Robeson, S.M. and D.G. Steyn, 1990; Ryan, W.F. 1995; Shi, J. P. and R.M. Harrison, 1997). However, there are numerous environmental processes that exhibit significant non-linear behaviour. Advances in the field of Artificial Neural Networks (ANN) in the late 1980s po...

In this paper we evaluate the Hidden Neural Network HMM/NN hybrid presented at last years ICASSP on two speech recognition benchmark tasks; 1) task independent isolated word recognition on the PHONEBOOK database, and 2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how Hidden Neural Networks (HNNs) with much fewer parameters than conventional HMM...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

The use of linear models has always been common practice in science and engineering. A good linear model, however, describes the dynamics of the system only in the neighborhood of the particular operating point for which the model was derived. The need a broader picture of the dynamics of real systems has prompted the development and use of dynamical which include the nonlinear interactions obs...

This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN appr...

ARTIFICIAL neural network (NN) architectures have been recognized for a number of years as a powerful technology for solving real-world image processing problems. The primary purpose of this special issue is to demonstrate some recent success in solving image processing problems and hopefully to motivate other image processing researchers to utilize this technology to solve their real-world pro...