We describe some aspects of a Broadcast News recognition system based on hybrid HMM/MLP acoustic modeling. These include the use of novel ‘modulation spectrogram’ features which are combined with conventional models at the posterior probability level, some experiments with nonlinear segment normalization, and an investigation of the interaction of model size and training set size for an multila...

In this paper two popular time series prediction methods – the Auto Regression Moving Average (ARMA) and the multilayer perceptron (MLP) – are compared while forecasting seven real world economical time series. It is shown that the prediction accuracy of both methods is poor in ill-structured problems. In the well-structured cases, when prediction accuracy is high, the MLP predicts better provi...

The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, the learning is unsupervised. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information con...

This paper present a new automated detection method for cardiac arrhythmia. The detection system is implemented with integration of feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using autoregressive (AR) and multivariate autoregressive (MVAR) modeling of one-lead and two-lead electrocardiogram si...

Radial Basis function Neural Networks forms a class of neural networks which is much more advantageous then other methods of neural networks such as faster learning, easy networks & structures & better approximations & classifications. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically ...

We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space an...

In this paper a new scheme of feature ranking and hence feature selection using a Multilayer Perceptron (MLP) Network has been proposed. The novelty of the proposed MLP-based scheme and its difference from another MLP-based feature ranking scheme have been analyzed. In addition we have modified an existing feature ranking/selection scheme based on fuzzy entropy. Empirical investigations show th...

The paper presents an S-Transform based multilayer perceptron neural network (MLP) classifier for the identification of power quality (PQ) disturbances.The proposed method is used to extract the three input features (Standard deviation, peak value and variances) from the distorted voltage waveforms simulated using parametric equations. The features extracted through S-transform are trained by a...

Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle...

A piecewise linear neural network (PLNN) is discussed which maps N-dimensional input vectors into Mdimensional output vectors. A convergent algorithm for designing the PLNN from training data is described. The design algorithm is based on a variation of backtracking algorithm known as the ‘branch and bound’ method. The performance of the PLNN is compared with that of a multilayer perceptron (ML...