Research
Areas
Artificial
Neural Network Modeling
Christos
N. Schizas, and Costas Neocleous (Higher Technical
Institute, Cyprus), Ibit Esat (Brunel University,
UK).

A generic neuronal model using
a block form of operational diagrams is investigated.
This formalism is used as an aid for classifying neuronal
models according to their idiosyncrasies: architecture;
activation function and processing element; and the
learning rule. The power of this representation is
determined by the fact that an easy identification
and extraction of the most essential features of single
neuron models is allowed. In addition, this representation
enables the identification of the truly novel model
that have been so far introduced. Furthermore, the
generic model facilitates the development of new neuronal
structures.
The models are initially presented
in a mathematical form, in either continuous or discrete
timespace state form, and their performance is analyzed
by simulating them using SIMULINK.
The investigated model is believed to be generic enough
to be a "parent" of most of the existing models and
at the same time detailed enough to appropriately
display all important properties of different models
introduced so far. For achieving a uniform basis for
comparisons, a common notation, based on the control
systems representation formalism is used. The adoption
of block diagrams employing simple component processes
introduces a different understanding of neuronal modeling
through graphical visualization. This provides an
improved understanding of the structure of a model
and thus achieves a deeper insight to the relation
between structure and dynamics.

Theory
and Applications of Genetic Algorithms
Christos
N. Schizas, Constantinos S. Pattichis, and K. Christodoulou
(Cyprus
Institute of Neurology and Genetics)

The
objective of this project is to investigate, how geneticsbased
machine learning (GBML) can be applied for diagnosing
certain neuromuscular disorders based on Electromyographic
(EMG) data. The effect of GBML control parameters
on diagnostic performance is also examined. A hybrid
diagnostic system is introduced that combines features
from both neural network and GBML models. Such a hybrid
system provides the enduser with a robust and reliable
system, since its diagnostic performance relies on
more than one learning principle.
In
the clinical EMG laboratory, 680 Motor Unit Action
Potentials MUAPs) were collected from 12 normal subjects,
11 motor neuron disease, and 11 myopathy subjects.
Each subject was described by a 14 component feature
vector consisting of the mean and the standard deviation
of each of the following MUAP parameters: duration,
spike duration; amplitude; area; spike area; phases;
and turns. More than a thousand GBML models were developed
by varying the following parameters: message length
size; number of classifiers; lifetax; period of introducing
the genetic algorithm (GA); (expressed in iterations,
showing how often the classifier system calls the
GA); crossover probability, and mutation probability.


Title
:

Creativity
in design and artificial neural networks. 
Authors
:

Costas
Neocleous, Ibit Esat, Christos N. Schizas 
Abstract
:

The
creativity phase is identified as an integral part of
the design phase. The characteristics of creative persons
which are relevant to designing artificial neural networks
manifesting aspects of creativity, are identified. Based
on these identifications, a general framework of artificial
neural network characteristics to implement such a goal
are proposed. 
Presented
/ Published :

Proc
of the Second World Conference “Integrated Design and
Process Technology”. 
Place
/ Date :

Austin,
Texas. 1996


Key
People
