Special issue
of Evolving Systems on
Applications
of Evolving Connectionist Systems
Guest Editor
Michael J. Watts
University of Adelaide, Australia
mjwatts@ieee.org
Scope
The topic of
this special issue is “Applications of
Kasabov’s Evolving Connectionist Systems”.
In
modern society, the volume and rate of data production are
huge and set to increase. To process and utilise this avalanche of
data,
methods are needed that can rapidly and accurately model it as it
becomes
available. These models must be able to learn throughout their
lifetimes,
without forgetting what they have previously learned, and be able to
explain
themselves.
Kasabov’s
Evolving Connectionist
Systems (ECoS) are able to
fulfil each of these requirements. They are a class of constructive
neural
networks that learn via structural growth and adaptation. They have a
fast,
one-pass learning algorithm, where all that can be learnt from the data
is
learned in the first training pass. Because of their open structure,
they
exhibit continuous, life-long learning whereby the structure expands as
necessary to accommodate new data. Finally, they have a strong
resistance to
catastrophic forgetting following additional training on new data.
Examples of ECoS networks include the
Evolving Fuzzy Neural
Network (EFuNN), which was the first ECoS network published and is
characterised by embedded fuzzy logic elements. There is also the
Simple
Evolving Connectionist System (SECoS), which is essentially an EFuNN
with the
fuzzy elements removed, and the Dynamic Evolving Fuzzy Inference System
(DENFIS) for discovering Takagi-Sugeno style fuzzy rules. Many ECoS
networks
use fuzzy rule extraction algorithms that allow for the explanation of
what the
networks have learned, in a comprehensible manner.
ECoS networks are well suited to
applications that are dealing
with new data continuously and that have dynamic, time-critical
aspects.
Previous applications of ECoS include:
- Stock
market prediction and macroeconomic modelling
- Speech
recognition, especially multi-speaker
speech recognition
- Bioinformatics
and medical modelling
- Image
and video parsing
- Robot
control
- Information
system security
The special issue is concerned
with
all aspects of the
application of ECoS networks to real-life problems and data sets.
Topics of
interest include, but are not limited to:
- Applications
of ECoS to real-world problems
- Data
mining of complex data sets using ECoS
- Comparisons
of ECoS with other algorithms over
real-world data sets
- Modifications
of ECoS algorithms to fit them to
real-world problems
Proposed
Schedule
- Submission due
date: 14 May, 2012
- Preliminary
notification of acceptance: 4 June, 2012
- Revised
manuscripts due: 9 July, 2012
- Final
acceptance
notification: 6 August, 2012
- Final version
due:
3 September, 2012
- Intended
publication date: January, 2013
Submission
The
special issue invites
original
contributions within the
specified scope. Manuscripts must not be under review elsewhere, nor
can they
have been previously published. Extended conference papers must contain
at
least 30% new material. Please format all manuscripts according to the
Instructions for Authors:
http://www.springer.com/physics/complexity/journal/12530
Please submit
all papers via the
online submission system:
https://www.editorialmanager.com/evos/
Maintained by Dr Michael J. Watts
