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 Programme
Overview
Detailed
Programme
| Neural Networks
in Robotics and Control (NNRC) |
CHAIR: NIGEL STEELE
Time: Wednesday,
March 23rd, 16h00-17h45
| NNRC-1 |
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| Title: |
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Applying Neural Network to Inverse
Kinematic Problem for 6R Robot Manipulator with Offset Wrist
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| Author(s): |
Z. Bingul,
H. M. Ertunc |
| Abstract: |
An Artificial Neural Network (ANN) using backpropagation
algorithm is applied to solve inverse kinematic problems of
industrial robot manipulator. 6R robot manipulator with offset
wrist was chosen as industrial robot manipulator because geometric
feature of this robot does not allow to solve inverse kinematic
problems analytically. In other words, there is no closed
form solution for this problem. As the number of neurons at
hidden layer is varied between 4 and 32, the robot joint angles
were predicted with average errors of 8.9o, 7.8o, 8.3o, 13o,
8.5o, and 10.5o for the 1st, 2nd, 3rd,4th and 6th joint, respectively. |
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| NNRC-2 |
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| Title: |
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Local Cluster Neural Network Chip
for Control |
| Author(s): |
Liang Zhang,
Joaquin Sitte,
Ulrich Rueckert |
| Abstract: |
The local cluster neural network (LCNN) is an
alternative to RBF networks that performs well in digital
simulation. The LCNN is suitable for an analog VLSI implementation
that is attractive for a wide range of embedded neural net
applications. In this paper, we present the input-output characterisation
of LCNN analog chip. The effect of manufacturing variations
on the chip's function is investigated and analyzed. |
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| NNRC-3 |
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| Title: |
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A Switching Controller for Nonlinear
Systems via Fuzzy Models |
| Author(s): |
M. Boumehraz,
K. Benmahammed |
| Abstract: |
A Lyapunov based switching control design method
for nonlinear systems using fuzzy models is proposed. The
switching controller consists of several linear state feedback
controllers; only one of the linear controllers is employed
at each moment according to a switching scheme. The gains
of the linear state feedback controllers are derived based
on Lyapunov stability theory. The fuzzy design model is represented
as a set of uncertain linear subsystems and then suffiency
conditions for the system to be globally stabilizable by the
switching controller are given.The proposed design method
is illustrated trogh numerical simulations of the chaotic
Lorenz system. |
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| NNRC-4 |
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| Title: |
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Competitive Decentralized Autonomous
Neural Net Controllers |
| Author(s): |
Takehiro Ohba,
Masaru Ishida |
| Abstract: |
A simple and effective method is proposed for controlling
a system consists of small processes. Each process is controlled
by a decentralized autonomous neural network controller. These
controllers compete with each other in order to increase their
performances. As a result of the competition, the performance
of whole system is kept at a suboptimal level. A control of
example system consist of lots of processes is performed. |
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| NNRC-5 |
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| Title: |
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Improved hierarchical fuzzy control
scheme |
| Author(s): |
Taher M. Jelleli,
Adel M. Alimi |
| Abstract: |
New modifications in mapping hierarchical fuzzy
control scheme are proposed, to get effective and optimized
control. The scheme was developed such that one can easily
understand and modify fuzzy rules in different levels of the
hierarchy. The presented approach ensures the universal pproximation
of functions in a compact domain. To validate this conceptual
approach, we consider a textile data base as a non linear,
multivariable and dynamic system. |
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| NNRC-6 |
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| Title: |
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On-line inference of finite automata
in noisy environments |
| Author(s): |
Ivan Gabrijel,
Andrej Dobnikar |
| Abstract: |
The most common type of noise in continuous
systems of the real world is Gaussian noise, whereas discrete
environments are usually subject to noise of a discrete type.
The established solution for on-line inference of finite automata
that is based on generalized recurrent neural networks is
evaluated in the presence of noise of both types. It showed
quite good performance and robustness. |
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