Artificial neural networks: Architectures and applications

264 Pages·11.017 MB·English

by  Kenji Suzuki (editor)

ARTIFICIAL NEURAL
NETWORKS
ARCHITECTURES
AND
APPLICATIONS
Edited by Kenji Suzuki
ARTIFICIAL NEURAL
NETWORKS –
ARCHITECTURES AND
APPLICATIONS
Edited by Kenji Suzuki
Artificial Neural Networks – Architectures and Applications
http://dx.doi.org/10.5772/3409
Edited by Kenji Suzuki
Contributors
Eduardo Bianchi, Thiago M. Geronimo, Carlos E. D. Cruz, Fernando de Souza Campos, Paulo Roberto De Aguiar, Yuko
Osana, Francisco Garcia Fernandez, Ignacio Soret Los Santos, Francisco Llamazares Redondo, Santiago Izquierdo
Izquierdo, José Manuel Ortiz-Rodríguez, Hector Rene Vega-Carrillo, José Manuel Cervantes-Viramontes, Víctor Martín
Hernández-Dávila, Maria Del Rosario Martínez-Blanco, Giovanni Caocci, Amr Radi, Joao Luis Garcia Rosa, Jan Mareš,
Lucie Grafova, Ales Prochazka, Pavel Konopasek, Siti Mariyam Shamsuddin, Hazem M. El-Bakry, Ivan Nunes Da Silva, Da
Silva
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Technical Editor InTech DTP team
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First published January, 2013
Printed in Croatia
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Artificial Neural Networks – Architectures and Applications, Edited by Kenji Suzuki
p. cm.
ISBN 978-953-51-0935-8

Contents
Preface VII
Section 1 Architecture and Design 1
Chapter 1 Improved Kohonen Feature Map Probabilistic Associative
Memory Based on Weights Distribution 3
Shingo Noguchi and Osana Yuko
Chapter 2 Biologically Plausible Artificial Neural Networks 25
João Luís Garcia Rosa
Chapter 3 Weight Changes for Learning Mechanisms in Two-Term
Back-Propagation Network 53
Siti Mariyam Shamsuddin, Ashraf Osman Ibrahim and Citra
Ramadhena
Chapter 4 Robust Design of Artificial Neural Networks Methodology in
Neutron Spectrometry 83
José Manuel Ortiz-Rodríguez, Ma. del Rosario Martínez-Blanco, José
Manuel Cervantes Viramontes and Héctor René Vega-Carrillo
Section 2 Applications 113
Chapter 5 Comparison Between an Artificial Neural Network and Logistic
Regression in Predicting Long Term Kidney
Transplantation Outcome 115
Giovanni Caocci, Roberto Baccoli, Roberto Littera, Sandro Orrù,
Carlo Carcassi and Giorgio La Nasa
Chapter 6 Edge Detection in Biomedical Images Using
Self-Organizing Maps 125
Lucie Gráfová, Jan Mareš, Aleš Procházka and Pavel Konopásek
VI Contents
Chapter 7 MLP and ANFIS Applied to the Prediction of Hole Diameters in
the Drilling Process 145
Thiago M. Geronimo, Carlos E. D. Cruz, Fernando de Souza Campos,
Paulo R. Aguiar and Eduardo C. Bianchi
Chapter 8 Integrating Modularity and Reconfigurability for Perfect
Implementation of Neural Networks 163
Hazem M. El-Bakry
Chapter 9 Applying Artificial Neural Network Hadron - Hadron
Collisions at LHC 183
Amr Radi and Samy K. Hindawi
Chapter 10 Applications of Artificial Neural Networks in Chemical
Problems 203
Vinícius Gonçalves Maltarollo, Káthia Maria Honório and Albérico
Borges Ferreira da Silva
Chapter 11 Recurrent Neural Network Based Approach for Solving
Groundwater Hydrology Problems 225
Ivan N. da Silva, José Ângelo Cagnon and Nilton José Saggioro
Chapter 12 Use of Artificial Neural Networks to Predict The Business
Success or Failure of Start-Up Firms 245
Francisco Garcia Fernandez, Ignacio Soret Los Santos, Javier Lopez
Martinez, Santiago Izquierdo Izquierdo and Francisco Llamazares
Redondo
Preface
Artificial neural networks may probably be the single most successful technology in the last
two decades which has been widely used in a large variety of applications in various areas.
An artificial neural network, often just called a neural network, is a mathematical (or
computational) model that is inspired by the structure and function of biological neural
networks in the brain. An artificial neural network consists of a number of artificial neurons
(i.e., nonlinear processing units) which are connected to each other via synaptic weights (or
simply just weights). An artificial neural network can “learn” a task by adjusting weights.
There are supervised and unsupervised models. A supervised model requires a “teacher” or
desired (ideal) output to learn a task. An unsupervised model does not require a “teacher,”
but it learns a task based on a cost function associated with the task. An artificial neural
network is a powerful, versatile tool. Artificial neural networks have been successfully used
in various applications such as biological, medical, industrial, control engendering, software
engineering, environmental, economical, and social applications. The high versatility of
artificial neural networks comes from its high capability and learning function. It has been
theoretically proved that an artificial neural network can approximate any continuous
mapping by arbitrary precision. Desired continuous mapping or a desired task is acquired
in an artificial neural network by learning.
The purpose of this book is to provide recent advances of architectures, methodologies and
applications of artificial neural networks. The book consists of two parts: architectures and
applications. The architecture part covers architectures, design, optimization, and analysis
of artificial neural networks. The fundamental concept, principles, and theory in the section
help understand and use an artificial neural network in a specific application properly as
well as effectively. The applications part covers applications of artificial neural networks in a
wide range of areas including biomedical applications, industrial applications, physics
applications, chemistry applications, and financial applications.
Thus, this book will be a fundamental source of recent advances and applications of artificial
neural networks in a wide variety of areas. The target audience of this book includes
professors, college students, graduate students, and engineers and researchers in companies.
I hope this book will be a useful source for readers.
Kenji Suzuki, Ph.D.
University of Chicago
Chicago, Illinois, USA

Section 1
Architecture and Design