It is well known that metamodel or surrogate modeling techniques have been widely applied in engineering problems due to their higher efficiency. However, with the increase of the linearity and dimensions, it is difficult for the present popular metamodeling techniques to construct reliable metamodel and apply to more and more complicated high dimensional problems. Recently, neural networks (NN...

We present and evaluate new techniques for call admission control based on neural networks. The methods are applicable to very general models that allow heterogeneous traac sources and nite buuers. A feedforward neural network (NN) is used to predict whether or not accepting a requested new call would result in a feasible aggregate stream, i.e., one that sat-isses the QOS requirements. The NN i...

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. In this paper, we propose a scheme to integrate fuzzy c-means (FCM) clustering, principal component analysis (PCA) and neural networks (NN) for ECG beat classification. The PCA is used to decompose ECG signals into weighted sum of basic components that a...

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, ...

Neural networks have a wide range of applications in analog and digital signal processing Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions of NN.This project proposes a knowledge-based neural network (KBNN) modeling approach with new hyperbolic tangent function . The KB...

In this paper, a sensitivity analysis of artificial neural networks (NNs) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. To evaluate the accuracy of the proposed NN model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. The ...

This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumpti...

We have computed two Geophysical Model Functions (one for the vertical and one for the horizontal polarization) for the NSCAT scatterometer by using neural networks. These Neural Network Geophysical Model Functions (NN-GMF) were estimated with NSCAT scatterometer sigma-0 measurements collocated with ECMWF analyzed wind vectors during the period 15 January 1997 to 15 April 1997. We performed a S...

We introduce a novel predictive statistical modeling technique called Hybrid Radial Basis Function Neural Networks (HRBF-NN) as a forecaster. HRBF-NN is a flexible forecasting technique that integrates regression trees, ridge regression, with radial basis function (RBF) neural networks (NN). We develop a new computational procedure using model selection based on information-theoretic principles...