Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided by the pre-squashed outputs from a one hidden layer multi-layer perceptron (MLP) trained to recognise speech subunits (phonemes) has previously been shown to significantly increase ASR performance. An analogous approac...

Multi-layer feedforward neural networks with sigmoidal activation functions have been termed "universal function approximators". Although these types of networks can approximate any continuous function to a desired degree of accuracy, this approximation may require an inordinate number of hidden nodes and is only accurate over a finite interval. These short comings are due to the standard multi...

This paper proposes a new investigation on Gaussian mixture model (GMM) by comparing it with some preliminary experiments on multilayered perceptron network (MLP) with backpropagation learning algorithm (BKP) and dynamic time warping (DTW) techniques on Thai text-dependent speaker identification system. Three major identification engines are conducted on 50 speakers with isolated digits 0-9. Tr...

The performance of global affine and nonlinear transformations for speaker adaptation in a hidden Markov model (HMM) speech recognition system are compared in this paper. The nonlinear transformation was obtained with a multilayer perceptron network (MLP) which was trained during the adaptation process to transform the mean vectors of the HMMs such that the output probabilities of the HMMs for ...

Considering the grid manager's point of view,needsin terms ofprediction of intermittent energy like thephotovoltaic resourcecan be distinguishedaccording to theconsideredhorizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.). Through this work, we haveidentified methodologies using time series modelsfor thepredictionhorizonof ...

Sensor technology has been used in water environment, which comes into being a water environment wireless sensor monitoring network. Monitoring data in the network slowly change, so we propose a geographical energy-efficient multi-hop clustering fusion routing algorithm based on multilayer perceptron (MLP-GEEMHCFR) in this paper to reduce transmittingdata and save the network energy.The algorit...

We consider simple cube-curves in the orthogonal 3D grid of cells. The union of all cells contained in such a curve (also called the tube of this curve) is a polyhedrally bounded set. The curve’s length is defined to be that of the minimum-length polygonal curve (MLP) fully contained and complete in the tube of the curve. So far only one general algorithm called rubber-band algorithm was known ...

The multilayer perceptron (MLP) is a well established neural network model for supervised learning problems. Furthermore, it is well known that its performance for a given problem depends crucially on appropriately selecting the MLP architecture, which is typically achieved using cross-validation. In this work, we propose an incremental Bayesian methodology to address the important problem of a...

This paper 1 proposes a method to extract nonlinear discriminant features from given input measurements by using outputs of multilayer Perceptron (MLP). Linear Discriminant Analysis (LDA) is one of the best known methods to construct linear features which are suitable for class discrimination. Otsu showed that LDA can be extended to nonlinear if we can estimate Bayesian a posteriori probabiliti...

In recent years, the cryptographic community has explored new approaches of power analysis based on machine learning models such as Support Vector Machine (SVM), MultiLayer Perceptron (MLP) or Random Forest (RF). Realized experiments proved that the method based on MLP can provide almost 100% success rate after optimization. Nevertheless, this description of results is based on the first order ...