background: patients with chronic stable angina often have a state of sympathetic hyperactivity. it is considered associated with myocardial ischemia and disappears after ischemia elimination. the aim of this study was to investigate the changes in heart rate variability parameters, a noninvasive technique for the evaluation of the autonomic nervous system activity, after successful revasculari...

Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear does not capture the complex nonlinear relationship between predictor target It rare to find a hydrological application using group method data handling (GMDH) model, artificial bee colony (ABC) algorithm, ensemble technique, precisely predicting ungauged sites...

Residential sector is one of the energy-consuming districts countries that causes CO2 emission in large extent. In this regard, must be considered energy policy making related to reduction and other greenhouse gases. present work, residential three countries, including Indonesia, Thailand, Vietnam Southeast Asia, are discussed modeled by employing Group Method Data Handling (GMDH) Multilayer Pe...

This paper address the problem of dust storm detection based on multispectral image analysis from a probabilistic point of view. Two classifiers are designed, one based on classic probability theory and other based on a probabilistic computational intelligence approach. The first classifier is designed under the Maximum Likelihood Estimation (MLE) model, and the second with a Probabilistic Neur...

This paper presents the quantification of uncertain natural frequency for laminated composite plates by using a novel surrogate model. A group method of data handling in conjunction to polynomial neural network (PNN) is employed as surrogate for numerical model and is trained by using Latin hypercube sampling. Subsequently the effect of noise on a PNN based uncertainty quantification algorithm ...

Neural networks (NNs) have been increasingly used in recent years for the solving complex nonlinear problems. NNs are seen as an attractive alternative to process based modeling approaches, as they are able to extract an underlying relationship from the data when knowledge of physical process is lacking. The paper evaluates the predictive power of a model, which emulates an army commander on th...

* This work was partially supported by the Italian MURST. AbstractThe aim of this paper is to present a novel technique for defect identification by neural networks based on the classification of remote field effect eddy current (RFEC) data. We consider a kind of neural network that does not require a long training and is particularly well suited for fast classification, the Probabilistic Neura...

In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experien...

Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(tauN2) time implementation of the exact PNN, where tau is...

The condition of joints in steel truss bridges is critical to railway operational safety. available methods for the quantitative assessment different types joint damage are, however, very limited. This paper numerically investigates feasibility using a probabilistic neural network (PNN) and finite element (FE) model updating technique assess bridges. A two-step identification procedure develope...