Two time delay neural network (TDNN) based forecasting systems are proposed to perform dynamic bandwidth reservation for real-time, variable bit rate (VBR) video service in ATM networks. Both multilayered perceptron (MLP) and pi-sigma network (PSN) based systems are found to give highly reliable predictions even in a nonstationary environment. Their performance is quantiied through simulation e...

In this paper we present a Time-Delay Neural Network (TDNN) approach to phoneme recognition which is characterized by two important properties. 1) Using a 3 layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces. The TDNN learns these decision surfaces automatically using error backpropagation 111. 2) Th...

We have designed a writer-adaptive character recognition system for on-line characters entered on a touch-terminal. It is based on a Time Delay Neural Network (TDNN) that is rst trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor to an Optimal Hyperplane classi er, that can be easily retrained to peculiar writ...

neme recognition which is characterized by two important properties: 1.) Using a 3 layer arrangement of simple computing units, it can represent arbitrary nonlinear decision surfaces. The TDNN learns these decision surfaces automatically using error back-propagatioii[l]. 2.) he time-delay arrangement enables the network to discover acoustichonetic features and the temporal relationships between...

This paper describes a new approach for automatic speechreading. First, we use efficient, but effective representation of visible speech: a geometric lipshape model. Then we present an automatic objective method to merge phonemes that appear visually similar into visemes for our speaker. In order to determine visemes, we trained SOM using the Kohonen algorithm on each phoneme extracted from our...

We present a detailed theoretical study of the organization and structure of landmark sequences like promoters and splice junctions in Human DNA. An improved detection of these landmark sequences in genomic DNA is important for exon detection and gene assembly. The function of eukaryotic promoters as initiators for transcription and of splice sites as signals for RNA assembly are among of the m...

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of ...

Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recognition. The strength of the model can be attributed to its ability to effectively model long temporal contexts. However, current TDNN models are relatively shallow, which limits the modelling capability. This paper proposes a method of increasing the network depth by deepening the kernel used in ...

| Conventional speech recognition systems based on Multi Layer Percep-trons often use Time Delay Neural Networks (TDNN). TDNNs were rst used for speech recognition by Waibel et al., but long training times and large numbers of parameters that need careful adjustment make it hard to achieve good performance. In contrast, networks using Radial Basis Functions (RBF) can be constructed systematical...

In the dynamic global economy, the accuracy in forecasting the foreign currency exchange rates is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. The aim of this study is to identify a neural network model which has ability to predict the US Dollar against Sri Lankan Rup...