Artificial neural networks : a practical course

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 im...

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Bibliographic Details
Main Authors: Silva, Ivan Nunes Da, (Author), Spatti, Danilo Hernane, (Author), Andrade Flauzino, Rogerio, (Author), Liboni, Luisa Helena Bartocci, (Author), Reis Alves, Silas Franco dos, (Author)
Format: eBook
Language: English
Published: Switzerland : Springer, [2016]
Subjects:
Neural networks (Computer science)
elektronické knihy
electronic books
ISBN: 9783319431628
9783319431611
Physical Description: 1 online resource (xx, 307 pages) : illustrations (some color)

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245 0 0 |a Artificial neural networks :  |b a practical course /  |c Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves. 
264 1 |a Switzerland :  |b Springer,  |c [2016] 
264 4 |c ©2017 
300 |a 1 online resource (xx, 307 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Intro; Preface; Organization; Acknowledgments; Contents; About the Authors; Architectures of Artificial Neural Networks and Their Theoretical Aspects; 1 Introduction; 1.1 Fundamental Theory; 1.1.1 Key Features; 1.1.2 Historical Overview; 1.1.3 Potential Application Areas; 1.2 Biological Neuron; 1.3 Artificial Neuron; 1.3.1 Partially Differentiable Activation Functions; 1.3.2 Fully Differentiable Activation Functions; 1.4 Performance Parameters; 1.5 Exercises; 2 Artificial Neural Network Architectures and Training Processes; 2.1 Introduction 
505 8 |a 2.2 Main Architectures of Artificial Neural Networks2.2.1 Single-Layer Feedforward Architecture; 2.2.2 Multiple-Layer Feedforward Architectures; 2.2.3 Recurrent or Feedback Architecture; 2.2.4 Mesh Architectures; 2.3 Training Processes and Properties of Learning; 2.3.1 Supervised Learning; 2.3.2 Unsupervised Learning; 2.3.3 Reinforcement Learning; 2.3.4 Offline Learning; 2.3.5 Online Learning; 2.4 Exercises; 3 The Perceptron Network; 3.1 Introduction; 3.2 Operating Principle of the Perceptron; 3.3 Mathematical Analysis of the Perceptron; 3.4 Training Process of the Perceptron; 3.5 Exercises 
505 8 |a 3.6 Practical Work4 The ADALINE Network and Delta Rule; 4.1 Introduction; 4.2 Operating Principle of the ADALINE; 4.3 Training Process of the ADALINE; 4.4 Comparison Between the Training Processes of the Perceptron and the ADALINE; 4.5 Exercises; 4.6 Practical Work; 5 Multilayer Perceptron Networks; 5.1 Introduction; 5.2 Operating Principle of the Multilayer Perceptron; 5.3 Training Process of the Multilayer Perceptron; 5.3.1 Deriving the Backpropagation Algorithm; 5.3.2 Implementing the Backpropagation Algorithm; 5.3.3 Optimized Versions of the Backpropagation Algorithm 
505 8 |a 5.4 Multilayer Perceptron Applications5.4.1 Problems of Pattern Classification; 5.4.2 Functional Approximation Problems (Curve Fitting); 5.4.3 Problems Involving Time-Variant Systems; 5.5 Aspects of Topological Specifications for MLP Networks; 5.5.1 Aspects of Cross-Validation Methods; 5.5.2 Aspects of the Training and Test Subsets; 5.5.3 Aspects of Overfitting and Underfitting Scenarios; 5.5.4 Aspects of Early Stopping; 5.5.5 Aspects of Convergence to Local Minima; 5.6 Implementation Aspects of Multilayer Perceptron Networks; 5.7 Exercises; 5.8 Practical Work 1 (Function Approximation) 
505 8 |a 5.9 Practical Work 2 (Pattern Classification)5.10 Practical Work 3 (Time-Variant Systems); 6 Radial Basis Function Networks; 6.1 Introduction; 6.2 Training Process of the RBF Network; 6.2.1 Adjustment of the Neurons from the Intermediate Layer (Stage I); 6.2.2 Adjustment of Neurons of the Output Layer (Stage II); 6.3 Applications of RBF Networks; 6.4 Exercises; 6.5 Practical Work 1 (Pattern Classification); 6.6 Practical Work 2 (Function Approximation); 7 Recurrent Hopfield Networks; 7.1 Introduction; 7.2 Operating Principles of the Hopfield Network 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Neural networks (Computer science) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Silva, Ivan Nunes Da,  |e author. 
700 1 |a Spatti, Danilo Hernane,  |e author. 
700 1 |a Andrade Flauzino, Rogerio,  |e author. 
700 1 |a Liboni, Luisa Helena Bartocci,  |e author. 
700 1 |a Reis Alves, Silas Franco dos,  |e author. 
776 0 8 |i Print version:  |t Artificial neural networks.  |d Switzerland : Springer, [2016]  |z 9783319431611  |z 3319431617  |w (OCoLC)952981222 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-43162-8  |y Plný text 
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