Artificial Neural Networks

1. Front Matter

   Pages i-xx

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2. Architectures of Artificial Neural Networks and Their Theoretical Aspects

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 3-19

   3. Artificial Neural Network Architectures and Training Processes

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 21-28

   4. The Perceptron Network

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 29-40

   5. The ADALINE Network and Delta Rule

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 41-54

   6. Multilayer Perceptron Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 55-115

   7. Radial Basis Function Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 117-138

   8. Recurrent Hopfield Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 139-155

   9. Self-Organizing Kohonen Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 157-172

   10. LVQ and Counter-Propagation Networks

       • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

       Pages 173-187

   11. ART (Adaptive Resonance Theory) Networks

       • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

       Pages 189-206

3. Application of Artificial Neural Networks in Engineering and Applied Science Problems

   1. Front Matter

      Pages 207-207

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   2. Coffee Global Quality Estimation Using Multilayer Perceptron

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 209-213

   3. Computer Network Traffic Analysis Using SNMP Protocol and LVQ Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 215-219

   4. Forecast of Stock Market Trends Using Recurrent Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 221-227

   5. Disease Diagnostic System Using ART Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 229-233

   6. Pattern Identification of Adulterants in Coffee Powder Using Kohonen Self-organizing Map

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 235-239

   7. Recognition of Disturbances Related to Electric Power Quality Using MLP Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 241-246

   8. Trajectory Control of Mobile Robot Using Fuzzy Systems and MLP Networks

      • Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

      Pages 247-252

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

• Neural Networks Architectures
• Architectures of Artificial Neural Networks
• Perceptron Network
• Adaline Network
• Self-Organizing Maps
• Learning Vector Quantization (LVQ)
• Adaptive Resonance Theory (ART)
• Artificial Neural Networks Applications
• Computer Network Traffic Analysis

“The book under review is quite unique, covering many important topics usually omitted from introductory courses on artificial neural networks, and as such it is a valuable reference. … A major advantage of this volume is the interesting choice of examples used, most of which are not commonly considered in the artificial neural network literature.” (Sandro Skansi, Mathematical Reviews, April, 2018)



“This book would be very good for advanced undergraduate students, first-year graduate students, or for anyone wishing to learn about neural networks on their own. It was originally published in Brazil in Portuguese. … The exercises thoroughly test the readers’ understanding of the descriptive material. The practical examples address the training and use of the architecture in the chapter.” (Anthony J. Duben, Computing Reviews, April, 2017)

• DEPT OF ELECT & COMP ENG, UNIV OD SAO PAULO-USP/EESC/SEL DEPT OF ELECT & COMP ENG, SAO CARLO, SP, Brazil

  Ivan Nunes da Silva

• Dept of Electrical & Comp. Engineering, University of Sao Paulo – USP/EESC/SEL Dept of Electrical & Comp. Engineering, Sao Carlos, Brazil

  Danilo Hernane Spatti

• Dept of Electrical & Comp Engineering, University of Sao Paulo – USP/EESC/SEL Dept of Electrical & Comp Engineering, Sao Carlos, Brazil

  Rogerio Andrade Flauzino

• Dep of Electrical and Comp. Engineering, University of Sao Paulo – USP/EESC/SEL Dep of Electrical and Comp. Engineering, Sao Carlos, Brazil

  Luisa Helena Bartocci Liboni

• Dept of Electrical and Comp Engineering, University of Sao Paulo – USP/EESC/SEL Dept of Electrical and Comp Engineering, Sao Carlos, Brazil

  Silas Franco dos Reis Alves

IVAN NUNES DA SILVA was born in São José do Rio Preto, Brazil, in 1967. He graduated in computer science and electrical engineering at the Federal University of Uberlândia, Brazil, in 1991 and 1992, respectively. He received both M.Sc. and Ph.D. degrees in electrical engineering from the State University of Campinas (UNICAMP), Brazil, in 1995 and 1997, respectively. Currently, he is an Associate Professor at the University of São Paulo (USP). His research interests are within the fields of artificial neural networks, fuzzy inference systems, power system automation and robotics. He is also associate editor of the International Journal on Power System Optimization and Editor-in-Chief of the Journal of Control, Automation and Electrical Systems. He has published more than 400 papers in congress proceedings, international journals and book chapters.

DANILO HERNANE SPATTI was born in Araras, Brazil, in 1981. He graduated in electrical engineering from the São Paulo State University (UNESP), Brazil, in 2005. He received both M.Sc. and Ph.D. degrees in electrical engineering from the University of São Paulo (USP), Brazil, in 2007 and 2009, respectively. Currently, he is a Senior Researcher at the University of São Paulo. His research interests are artificial neural networks, computation complexity, systems optimization and intelligent systems.

ROGÉRIO ANDRADE FLAUZINO was born in Franca, Brazil, in 1978. He graduated in electrical engineering and also received M.Sc. degree in electrical engineering from the São Paulo State University (UNESP), Brazil, in 2001 and 2004, respectively. He received Ph.D. degree in electrical engineering from the University of São Paulo (USP), Brazil, in 2007. Currently, he is an Associate Professor at the University of São Paulo. His research interests are artificial neural networks, computational intelligence, fuzzy inference systems and power systems.

SILAS FRANCO DOS REIS ALVES was born in Marília, Brazil, in 1987. He graduated in information systems from the São Paulo State University (UNESP). He received M.Sc. degree in mechanical engineering from the State University of Campinas (UNICAMP) and Ph.D. degree in electrical engineering from the University of São Paulo (USP), Brazil, in 2011 and 2016, respectively. Currently, he is a Senior Researcher at the University of São Paulo. His research interests are robotics, artificial neural networks, machine learning, intelligent systems, signal processing and nonlinear optimization.

LUISA HELENA BARTOCCI LIBONI was born in Sertãozinho, Brazil, in 1986. She graduated in electrical engineering from the Polytechnic School of the University of São Paulo (USP), Brazil, in 2010. She received Ph.D. degree in electrical engineering from the University of São Paulo (USP), Brazil, in 2016. Currently, she is a Senior Researcher at the University of São Paulo. Her research interests are artificial neural networks, intelligent systems,signal processing and nonlinear optimization.

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