Computational Intelligence Laboratory
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:
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
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)
Computational Intelligence
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Computational intelligence combines elements of learning, adaptation, evolution and
fuzzy logic (rough sets) to create systemss that are, in some sense, intelligent.
Our research covers neural
networks, fuzzy systems and evolutionary computation, including swarm intelligence,
as well as their applications.Computational Neuroscience
Intelligent Control and Learning Control
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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.This laboratory contains
a cluster of multimedia Pentium PCs.
The laboratory is located in 910 SEO.
People from the Lab
Current members:
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