Computational Intelligence Laboratory
Keywords: Liu, D. Liu, Derong Liu,
computational intelligence,
intelligent systems, intelligent control,
reinforcement learning, machine learning,
adaptive critic designs, ACDs, neuro-dynamic programming, NDP,
approximate dynamic programming,
asymptotic dynamic programming,
adaptive dynamic programming, ADP,
neural networks,
recurrent neural networks, cellular neural networks, CNNs, associative memories,
control systems, nonlinear dynamical systems,
multimedia, traffic control, call admission control, CAC,
traffic modeling, performance analysis,
wireless networks, cellular networks, CDMA wireless networks,
power control,
broadband networks, ATM networks, video processing,
financial engineering
The mission of CIL is to develop
methods, tools, and technology
for the design and implementation of learning systems which mimic
the learning process of humans, and apply them to real world problems.
The goal is to solve complex engineering problems
which are difficult to deal with using conventional approaches.
Current application areas include traffic control/management in telecommunication networks
and learning control methodologies for automotive engines,
among others.
The emphasis in CIL is on collaboration with
researchers and practitioners from academia and industry.
Some of the current projects in the laboratory are:
A Welcome from Professor
Derong Liu
Welcome to the Computational Intelligence Laboratory (CIL) in
the
Department of Electrical and Computer Engineering at the
University of Illinois
near downtown Chicago, Illinois.
This laboratory was established in August 1999.
The purpose of setting up such a laboratory
is to provide a lab that supports a research program which strives to bring
together the results of academic research in
the field of computational intelligence and the problems encountered in
engineering practice.
If you have any questions,
comments, or suggestions, please send e-mail to
dliu@ece.uic.edu.
Computational Intelligence Laboratory (CIL)
Intelligent Control and Learning Control
Our current interest in this field is
neural network-based approaches for intelligent systems and learning control.
In particular, our study is focused on neural network-based
adaptive critic designs. Adaptive critic designs are designs
that approximate dynamic
programming in the general case, i.e., approximate optimal control
over time in noisy, nonlinear environments. There are many practical
problems that can be formulated as to minimize or maximize
a measure of cost.
It is well-known that dynamic programming is very useful in
solving these problems.
However, it is often computationally untenable to run true dynamic
programming due to the backward numerical process required for its
solutions, i.e., due to the "curse of dimensionality."
Over the years, progress has been made to circumvent the
"curse of dimensionality" by
building a system, called "critic" to approximate the cost
function in dynamic programming. The idea is to approximate dynamic
programming solutions by using a function approximation structure
such as neural networks to approximate the cost function.
Our work includes methodology
development and applications of adaptive
critic designs to automotive engine control.
Management and Control of Wireless Networks
One of our current funded research effort is to study
management and control problems in wireless networks.
Our focus in this study is the call admission control in these networks.
Call admission control schemes are critical to the success of future
generations of wireless networks. On one hand, call admission
control schemes provide the users (mobile or non-mobile)
with access to a network
for services. On the other hand, they are the decision making part of
the network carriers with the objectives of providing services to users
with guaranteed quality and at the same time, achieving as high as possible
resource utilization
(to the network carriers, higher
resource utilization simply means higher revenue).
The goal of our work is to
develop and implement new and improved call admission
control schemes for wireless
communication networks.
This laboratory contains
a cluster of multimedia Pentium PCs.
The laboratory is located in 1019-1021 SEO.
People from the Lab
Current members:
Derong Liu, Director
Ting Huang, Ph.D. Student
Ning Jin, Ph.D. Student
Zhuo Wang, Ph.D. Student
Visiting members:
Former members:
Research Support
Research in this laboratory has been supported by:
Useful Links
Author: Derong Liu E-Mail: dliu@ece.uic.edu