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 Programme
Overview
Detailed
Programme
| Hybrid Intelligent
Systems (HIS) |
CHAIR: ROMAN NERUDA
Monday, March 21st, 11h00-12h40
| HIS-1 |
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| Title: |
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Toward an On-Line Handwriting Recognition
System Based on Visual Coding and Genetic Algorithm |
| Author(s): |
M. Kherallah,
F. Bouri,
M.A. Alimi |
| Abstract: |
One of the most promising methods of interacting
with small portable computing devices, such as personal digital
assistants, is the use of handwriting. In order to make this
communication method more natural, we proposed to visually
observe the writing process on ordinary paper and to automatically
recover the pen trajectory from numerical tablet sequences.
On the basis of this work we developed handwriting recognition
system based on visual coding and genetic algorithm. The system
was specialized on Arabic script. In this paper we will present
the different steps of the handwriting recognition system.
We focus our contribution on genetic algorithm method. |
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| HIS-2 |
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| Title: |
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Multi-objective genetic algorithm
applied to the structure selection of RBFNN temperature estimators |
| Author(s): |
C. A. Teixeira,
W. C. A. Pereira,
A. E. Ruano,
M. Graca Ruano |
| Abstract: |
Temperature modelling of a homogeneous medium,
when this medium is radiated by therapeutic ultrasound, is
a fundamental step in order to analyse the performance of
estimators for in-vivo modelling. In this paper punctual and
invasive temperature estimation in a homogeneous medium is
employed. Radial Basis Functions Neural Networks (RBFNNs)
are used as estimators. The best fitted RBFNNs are selected
using a Multi-objective Genetic Algorithm (MOGA). An absolute
average error of 0.0084 ºC was attained with these estimators. |
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| HIS-3 |
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| Title: |
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Assessing the Reliability of Complex
Networks through Hybrid Intelligent Systems |
| Author(s): |
D. Torres,
C. Rocco |
| Abstract: |
This paper describes the application of Hybrid
Intelligent Systems in a new domain: reliability of complex
networks. The reliability is assessed by employing two algorithms
(TREPAN and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)),
both belonging to the Hybrid Intelligent Systems paradigm.
TREPAN is a technique to extract linguistic rules from a trained
Neural Network, whereas ANFIS is a method that combines fuzzy
inference systems and neural networks. In the experiment presented,
the structure function of the complex network analyzed is
properly emulated by training both models on a subset of possible
system configurations, generated by a Monte Carlo simulation
and an appropriate Evaluation Function. The experiments show
that, both approaches are able to successfully describe the
network status through a set of rules, which allows the reliability
assessment |
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| HIS-4 |
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| Title: |
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Autonomous Behavior of Computational
Agents |
| Author(s): |
R. Vaculin,
R. Neruda |
| Abstract: |
In this paper we present an architecture for
decision making of software agents that allows the agent to
behave autonomously. Our target area is computational agents
- encapsulating various neural networks, genetic algorithms,
and similar methods - that are expected to solve problems
of different nature within an environment of a hybrid computational
multi-agent system. The architecture is based on the vertically-layered
and belief-desire-intention architectures. Several experiments
with computational agents were conducted to demonstrate the
benefits of the architecture. |
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| HIS-5 |
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| Title: |
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Neural Network Generating Hidden
Markov Chain |
| Author(s): |
J. Koutnik,
M. Snorek |
| Abstract: |
In this paper we introduce technique how a neural
network can generate a Hidden Markov Chain. We use a neural
network called Temporal Information Categorizing and Learning
Map. The network is an enhanced version of standard Categorizing
and Learning Module (CALM). Our modifications include Euclidean
metrics instead of weighted sum formerly used for categorization
of the input space. Construction of a Hidden Markov Chain
is provided by turning steady weight internal synapses to
associative learning synapses. Result obtained form testing
on simple artificial data promise applicability in a real
problem domain. We present a visualization technique of the
obtained Hidden Markov Chain and a method how the results
can be validated. Experiments are being performed. |
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