University of Houston
Department of Computer Science
In
partial fulfillment of the Requirements for the Degree of
Master of
Science
Zhanshou Yu
will defend his
thesis
Feed-Forward Neural Networks and Their Applications in Forecasting
Abstract
Neural networks are computational models with the capacity to learn, to generalize,
or to organize data based on parallel processing. Among all kinds of networks,
the most widely used are multi-layer feed-forward neural networks that are capable
of representing non-linear functional mappings between inputs and outputs and are
hailed as "Universal Approximators". These networks can be trained with a powerful
and computationally efficient method called error back-propagation.
In this thesis, a multi-layer feed-forward neural network based gas load forecast
model - the TellFuture load forecast system, is built with Java to show how neural networks
work in forecasting. It is known that gas load depends on many factors such as weather,
calendar, and other economic information. The model will capture those effects, reflect
them within the system, and provide valuable future forecasting data. Similar models
can be built to solve problems in other fields as long as the correct relationship between the inputs and the outputs can be captured.
Date: Thursday, November 9, 2000
Time: 10:00 AM
Place:
550-PGH
Faculty, students, and the general public are
invited.
Thesis Advisor: Dr. Olin Johnson