Two schemes to obtain phonemic transcriptions of spoken utterances are described and compared. Both schemes utilize the so called Self-Organizing Kohonen Maps first to vector quantize speech into a sequence of phoneme Iabels centisecond apart. In the original scheme, this quasiphoneme sequence is converted into a phoneme string with simple durational transformation rules. In the scheme introduc...

Data processing and management is common now a days. In this paper, automatic processing of forms written in Kannada language is considered. A suitable pre-processing technique is presented for extracting handwritten characters. Principal Component Analysis (PCA) and Histogram of oriented Gradients (HoG) are used for feature extraction. These features are fed to multilayer feed forward back pro...

It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS) during GPS outages, a novel prediction algorithm of dead reckon (DR) position error is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. DR, GPS position data are first denoised and compared at different stationary wavelet transformation (SWT) decomposition level, ...

In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introd...

The recurrent neural network is a feed-forward network ascribed to a parent neural network with feed-back connections (or in another term, oriented cycles). Its adaptation is performed by an analog of the standard back-propagation adaptation method. The recurrent neural network approach is illustrated by prediction and classification of 13C NMR chemical shifts in a series of monosubstituted ben...

The suitability of Back Propagation Neural Network (BPNN) for classification of remote sensing images is explored in this paper. An approach that consists of three steps to classify IRS-1D images is proposed. In the first step, features are extracted from the firstorder histogram measures. The next step is feature classification based on BPNN, and in the finally step the results are compared wi...

A neural net program for pattern classification is presented, which includes: i) an improved version of Kohonen's Learning Vector Quantization (LVQ with Training Count); ii) Feed-Forward Neural Networks with Back-Propagation training; iii) Gaussian (or Mahalanobis distance) classification; iv) Fisher linear discrimination. Back-Prop trainings with emulations of Intel's ETANN and Siemens' MA16 n...