Papers available on SOM and BSS-ICA

[Simone G.O. Fiori]

Dear Connectionists, 
 the following two papers are now available:


 "A Review of Artificial Neural Networks Applications
  in Microwave Computer-Aided Design"
 
 by Pietro Burrascano, Simone Fiori, and Mauro Mongiardo
 University of Perugia, Perugia - Italy

                        Abstract

Neural networks found significant applications in microwave 
CAD. In this paper, after providing a brief description of 
neural networks employed so far in this context, we illustrate
some of their most significant applications and typical issues
arising in practical implementation. We also summarize current
research tendencies and introduce use of self-organizing maps 
(SOM) enhancing model accuracy and applicability. We conclude 
considering some future developments and exciting perspectives 
opened from use of neural networks in microwave CAD.

                        Keywords

Artificial neural networks; Self-organizing maps; Microwave
components; Filter design.

                        Journal

International Journal of RF and Microwave CAE, Vol. 9, 
pp. 158 -- 174, 1999

==============================================================

 "Entropy Optimization by the PFANN Network: Application to
 Blind Source Separation"

 by Simone Fiori
 University of Perugia, Perugia - Italy

                        Abstract

The aim of this paper is to present a study of polynomial
functional-link neural units that learn through an information-
theoretic-based criterion. First the structure of the neuron is
presented and the unsupervised learning theory is explained and
discussed, with particular attention being paid to its probability
density function and cimulative distribution function approximation
capability. Then a neural network formed by such neurons (the
polynomial functional-link artificial neural network, or PFANN) is 
shown to be able to separate out lienarly mixed eterokurtic source 
signals, i.e. signals endowed with either positive or negative 
kurtoses. In order to compare the performance of the proposed blind 
separation technique with those exhibited by existing methods, the 
mixture of densities (MOD) approach of Xu et al, which is closely 
related to PFANN, is briefly recalled; then comparative numerical
simulations performed on both synthetic and real-world signals and
a complexity evaluation are illustrated. These results show that the 
PFANN approach give similar performance with a noticeable reduction
in computational effort.

                        Journal

Network: Computation in Neural Systems, Vol. 10, No. 2,
pp. 171 -- 186, 1999


Requests of reprints should be addressed to:

Dr. Simone Fiori
Neural Networks Research Group at the
Dept. of Industrial Engineering
University of Perugia - Perugia, Italy
Loc. Pentima bassa, 21 I-05100, TERNI
E-mail: simone at eealab.unian.it, sfr at unipg.it

Best regards,
Simone