at which control information is made available will be more crucial than the time taken to build and update the environmental model. For these reasons it would seem natural to build a dynamic vision system for autonomous navigation with two loosely connected components with very different temporal rates that reflect their required behaviours. This approach follows roughly the ideas of a subsump...

Feed Forward Neural Networks (FFNNs) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one nite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13 th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. However, as the...

Institute of Transportation Studies, University of California, Irvine, Calif. 92717. Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic d...

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall runoff processes. However, the employment of a single model does not seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process that varies in space and time. For this reason, this study aims at...

In this work we look for a general neural network model that resembles the interactions between glucose concentration levels and amount of insulin injected in the bodies of diabetics. We use real data for 70 different patients of diabetics and build on it our model. Two types of neural networks (NN’s) are experimented in building that model; the first type is called the Levenberg-Marquardt (LM)...

Among the many dimensionality reduction techniques that have appeared in the statistical literature, multidimensional scaling and nonlinear mapping are unique for their conceptual simplicity and ability to reproduce the topology and structure of the data space in a faithful and unbiased manner. However, a major shortcoming of these methods is their quadratic dependence on the number of objects ...

Artificial Neural Networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. ANNs can be used as function approximators to estimate both the time and spatial air temperature distributions based on observational data. After reviewing the theoretical background as well as ...

This paper aimed to design and implement a new hybrid intelligent technique called expert network. The main purpose of this expert network is to use it as a management tool assist network administrator in his job regarding the monitoring process of network performance. Here, the management task that uses this expert network was how to detect a fault (i.e. fault detection) that may be occurred o...

This paper presents experimental comparison between selected neural architectures for chaotic time series prediction problem. Several feed-forward architectures (Multilayer Perceptrons) are compared with partially recurrent nets (Elman, extended Elman, and Jordan) based on convergence rate, prediction accuracy, training time requirements and stability of results. Results for chaotic logistic ma...

The most common way of human-to-human communication is speech. As speech provides the easiest and most natural way of interaction, it becomes the need of human-to-machine communication as well. Automatic speech recognition (ASR) is the technology to enable machines to understand process and recognize speech. Due to its applicability in various application domains, ASR becomes one of the most fa...