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2017 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Artificial Neural Networks

A Practical Course

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

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Architectures of Artificial Neural Networks and Their Theoretical Aspects

Frontmatter
Chapter 1. Introduction
Abstract
Building a machine or autonomous mechanism endowed with intelligence is an ancient dream of researchers from the diverse areas of sciences and engineering.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 2. Artificial Neural Network Architectures and Training Processes
Abstract
The architecture of an artificial neural network defines how its several neurons are arranged, or placed, in relation to each other. These arrangements are structured essentially by directing the synaptic connections of the neurons.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 3. The Perceptron Network
Abstract
The Perceptron, created by Rosenblatt, is the simplest configuration of an artificial neural network ever created, whose purpose was to implement a computational model based on the retina, aiming an element for electronic perception. One application of the Perceptron was to identify geometric patterns.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 4. The ADALINE Network and Delta Rule
Abstract
The ADALINE (Adaptive Linear Element) was created by Widrow and Hoff in 1960. Its main application was in switching circuits of telephone networks, which was one of the first industrial applications that effectively involved artificial neural networks (Widrow and Hoff 1960).
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 5. Multilayer Perceptron Networks
Abstract
Multilayer Perceptron (MLP) network features, at least, one intermediate (hidden) neural layer, which is placed between the input layer and the respective output layer.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 6. Radial Basis Function Networks
Abstract
Radial Basis Function networks, commonly known as RBF, can also be employed in almost every kind of problems solved by MLPs, including those involving curve fitting and pattern classification.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 7. Recurrent Hopfield Networks
Abstract
As mentioned in Sect. 2.​2.​3, recurrent neural networks are those which the outputs of a neural layer can be fed back to the network inputs.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 8. Self-Organizing Kohonen Networks
Abstract
However, in some particular applications, only a set of input samples is available, not being available their corresponding desired outputs.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 9. LVQ and Counter-Propagation Networks
Abstract
In this chapter, two architectures of artificial neural networks with supervised learning are presented. One of them, the LVQ (Learning Vector Quantization) network has, in its learning process, certain similarities to the Kohonen self-organizing network, and it has usually been applied to pattern classification problems.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 10. ART (Adaptive Resonance Theory) Networks
Abstract
The Adaptive resonance theory (ART), initially proposed by Grossberg (1976a, b), was developed from the observation of some biological phenomena, regarding vision, speech, cortical development, and cognitive-emotional interactions.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

Application of Artificial Neural Networks in Engineering and Applied Science Problems

Frontmatter
Chapter 11. Coffee Global Quality Estimation Using Multilayer Perceptron
Abstract
This application uses artificial neural networks for qualifying coffee batches or coffee brands from a set of sensors based on conductive polymers, which were developed by EMBRAPA (Brazilian Agricultural Research Corporation).
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 12. Computer Network Traffic Analysis Using SNMP Protocol and LVQ Networks
Abstract
Investigations of data flow in a computer network are essential for planning the system expansion, as well as for solving problems.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 13. Forecast of Stock Market Trends Using Recurrent Networks
Abstract
Conventional methods for predicting the behavior of financial papers are based on specialist’s analysis and decision-making, and automatic methods are unavailable for most situations.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 14. Disease Diagnostic System Using ART Networks
Abstract
Specification of the correct treatment for people with disorders can be very complex due to the diversity of disorders.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 15. Pattern Identification of Adulterants in Coffee Powder Using Kohonen Self-organizing Map
Abstract
The purpose of this application is to identify adulterant patterns found in samples of roasted ground coffee using the Kohonen network.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 16. Recognition of Disturbances Related to Electric Power Quality Using MLP Networks
Abstract
The technical and scientific evaluation of power quality (PQ) had a great advance after the use of intelligent systems.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 17. Trajectory Control of Mobile Robot Using Fuzzy Systems and MLP Networks
Abstract
Nowadays, several control methods are used in automation; however, not all of them respond in an expected way if the plant does not present a linear response.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 18. Method for Classifying Tomatoes Using Computer Vision and MLP Networks
Abstract
Quality control in a production line, when performed in a nonautomatic way, is subjected to mistakes made by the inspection agents, who over time and due to fatigue let products that are improper for human consumption pass through the mats.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 19. Performance Analysis of RBF and MLP Networks in Pattern Classification
Abstract
Both the RBF network and the MLP network can be used as pattern classifiers in situations where there are high-dimensional input spaces.
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Chapter 20. Solution of Constrained Optimization Problems Using Hopfield Networks
Abstract
Constrained optimization problems refer, generally, to maximize or minimize an objective function subjected to a set of equality, and/or inequality constraints (linear or nonlinear) (Bazaraa and Shetty 1979).
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
Backmatter
Metadaten
Titel
Artificial Neural Networks
verfasst von
Ivan Nunes da Silva
Danilo Hernane Spatti
Rogerio Andrade Flauzino
Luisa Helena Bartocci Liboni
Silas Franco dos Reis Alves
Copyright-Jahr
2017
Electronic ISBN
978-3-319-43162-8
Print ISBN
978-3-319-43161-1
DOI
https://doi.org/10.1007/978-3-319-43162-8

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