This paper shows a way to combine speech recognition techniques based on Vector Quantization (VQ) with Neural Networks (NN). Vector Quantization has proved its usefulness for isolated words recognition, but it is also useful for isolated sentences recognition. One way to improve the performance of this technique is to add an NN block that will help the performance of the VQ recognizer.

A quasi-linear ARX neural network model (QARXNN) is a nonlinear model built using neural networks (NN). It has a linear-ARX structure where NN is an embedded system to give the parameters for the regression vector. There are two contributions in this paper, 1) Hierarchical Algorithms is proposed for the training of QARXNN model, 2) an adaptive learning is implemented to update learning rate in ...

This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (P...

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and ou...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

Electromyograph (EMG) features have the properties of large variations and nonstationarity. An important issue in the classification of EMG is the classifier design. The major goal of this paper is to develop a classifier for the classification of eight kinds of prehensile postures to achieve high classification rate and reduce the online learning time. The cascaded architecture of neural netwo...

A general approach for system identification with artificial neural networks is presented. A dynamic process can be represented as a function of the evolution of the inputs/outputs of the system. The standard approach to system identification is to describe the system by a set of equations that take into account the underlying mechanisms of the studied plant. If the equations of the system are ...

Due to the large amount of data and the complexity of the information , analysis of the HEGRA Geiger tower data is based on neural networks (NN). We explain the NN training and the principal characteristics of the analysis and describe rst applications to experimental data, yielding an upper limit on the combined-ray ux above 50 TeV of 16 nearby northern blazars.

The most commonly used objective function in Artificial Neural Networks (ANNs) is the sum of squared errors. This requires the target and forecasted output vector to have the same dimension. In the context of nonlinear financial time series, both conditional mean and variance (volatility) tend to evolve over time. It is therefore of interest to consider neural networks with two-dimensional outp...

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. We present an Ensemble Neural Network (Neural-Fusion) solution which compares favorably with other methods. We propose a co-evolutionary system to design Neural Networks Ensemble. This method addresses the issues of automatic determination of the number of...