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 Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2013 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Iva Lipovic Technical Editor InTech DTP team Cover InTech Design team First published January, 2013 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Artificial Neural Networks – Architectures and Applications, Edited by Kenji Suzuki p. cm. ISBN 978-953-51-0935-8 free online editions of InTech Books and Journals can be found at www.intechopen.com 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 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 ContentsVI 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 [...]... also multipolar [57] 28 4 Artificial Neural Networks – Architectures and Applications Artificial Neural Networks Figure 6 A 3-layer neural network Notice that there are A + 1 input units, B + 1 hidden units, and C output units w1 and w2 are the synaptic weight matrices between input and hidden layers and between hidden and output layers, respectively The “extra” neurons in input and hidden layers, labeled... Analog Patterns (When “bear” was Given) 13 14 Artificial Neural Networks – Architectures and Applications Figure 9 One-to-Many Associations for Analog Patterns (When “mouse” was Given) Learning Pattern Long Radius aiShort Radius bi “bear – lion” 2.5 1.5 “bear – raccoon dog” 3.5 2.0 “bear – penguin” 4.0 2.5 “mouse – chick” 2.5 1.5 “mouse – hen” 3.5 2.0 “mouse – monkey” 4.0 2.5 Table 4 Area Size corresponding... to the pattern pairs including “crow”/“duck” are arranged in near area each other 11 12 Artificial Neural Networks – Architectures and Applications Learning Pattern Long Radius aiShort Radius bi “crow – lion” 2.5 1.5 “crow – monkey” 3.5 2.0 “crow – mouse” 4.0 2.5 “duck – penguin” 2.5 1.5 “duck – dog” 3.5 2.0 “duck – cat” 4.0 2.5 Table 2 Area Size corresponding to Patterns in Fig 3 Figure 6 Area Representation... distribution, and reproduction inThis is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited 26 2 Artificial Neural Networks – Architectures and Applications Artificial Neural Networks 1.1 McCulloch-Pitts... center is the neuron i In the KFMPAM-WD, ai and bi can be set for each training pattern mij is the slope of the line through the neurons i and j In Eq.(1), the neuron whose Euclidian distance between its connection weights and the learning vector is minimum in the neurons which can be take areas without 5 6 Artificial Neural Networks – Architectures and Applications overlaps to the areas corresponding... Eq.(1) In Eq.(1), the neuron whose Euclid distance between its connection weights and the learning vec‐ tor is minimum in the neurons which can be take areas without overlaps to the areas 7 8 Artificial Neural Networks – Architectures and Applications corresponding to the patterns which are already trained In Eq.(1), ai and bi are used as the size of the area for the learning vector 5 If d(X(p), Wr)... Layer and 900 neurons in the Map Layer Table 6 shows the learning time of the pro‐ posed model and the conventional model(16) These results are average of 100 trials on the Personal Computer (Intel Pentium 4 (3.2GHz), FreeBSD 4.11, gcc 2.95.3) As shown in Table 6, the learning time of the proposed model is shorter than that of the conventional model 21 22 Artificial Neural Networks – Architectures and Applications. .. the network composed of 800 neurons in the Input/Output Layer and 400 neurons in the Map Layer Figure 4 shows a part of the association result when “crow” was given to the Input/Output Layer As shown in Fig 4, when “crow” was given to the net‐ 9 10 Artificial Neural Networks – Architectures and Applications work, “mouse” (t=1), “monkey” (t=2) and “lion” (t=4) were recalled Figure 5 shows a part of the... distribution, and reproduction in any medium, provided the original work is properly cited 4 Artificial Neural Networks – Architectures and Applications ciations for plural sequential patterns including common terms [11, 12] Moreover, the KFM associative memory with area representation [13] has been proposed In the model, the area representation [14] was introduced to the KFM associative memory, and it has... Conference on Artificial Neural Networks, Vienna [12] Sakurai, N., Hattori, M., & Ito, H (2002) SOM associative memory for temporal se‐ quences Proceedings of IEEE and INNS International Joint Conference on Neural Net‐ works, 950-955, Honolulu [13] Abe, H., & Osana, Y (2006) Kohonen feature map associative memory with area rep‐ resentation Proceedings of IASTED Artificial Intelligence and Applications, . ARTIFICIAL NEURAL NETWORKS – ARCHITECTURES AND APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks – Architectures and Applications http://dx.doi.org/10.5772/3409 Edited. noisy in‐ put and damaged neurons. And, the learning considering the neighborhood can be realized. Artificial Neural Networks – Architectures and Applications4 2.