Applied Neural Networks and Soft Computing

234 Pages·2019·19.593 MB·English

by  Stanimirovic, Ivan

APPLIED NEURAL NETWORKS AND
SOFT COMPUTING

APPLIED NEURAL NETWORKS AND
SOFT COMPUTING
Ivan Stanimirović
ARCLER
P r e ss
www.arclerpress.com
Applied Neural Networks and Soft Computing
Ivan Stanimirović
Arcler Press
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e-book Edition 2019
ISBN: 978-1-77361-586-8 (e-book)
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ABOUT THE AUTHOR
Ivan Stanimirović gained his PhD from University of Niš, Serbia in 2013.
His work spans from multi-objective optimization methods to applications of
generalized matrix inverses in areas such as image processing and computer
graphics and visualisations. He is currently working as an Assistant professor
at Faculty of Sciences and Mathematics at University of Niš on computing
generalized matrix inverses and its applications.

TABLE OF CONTENTS
List of Figures ................................................................................................ix
List of Tables ...............................................................................................xvii
Preface........................................................................ ................................xix
Chapter 1 Introduction ..............................................................................................1
1.1. Differences Between The Brain and A Computer .................................4
1.2. Artificial Neural Networks ..................................................................7
1.3. Definition and Characteristics .............................................................7
1.4. Processing Stages ..............................................................................17
1.5. Training or Learning ..........................................................................22
Chapter 2 Application of an Intelligent Hopfield
Neural Networks For Face Recognition ...................................................29
2.1. Methods And Techniques In Face
Recognition Of Digital Images .......................................................31
2.2. Face Recognition Using Artificial Neural Networks ...........................33
2.3. Feature Extraction Techniques ...........................................................37
2.4. Pattern Recognition ...........................................................................43
2.5. Association and Classification ...........................................................53
2.6. Natural Language Processing ............................................................55
2.7. Network Layer: Perceptron, Adaline, And Madaline ..........................59
2.8. Backpropagation ...............................................................................66
2.9. Validation .........................................................................................72
Chapter 3 Artificial Neural Networks ......................................................................75
3.1. Introduction ......................................................................................76
3.2. Analogy With The Brain ....................................................................76
3.3. Neural Networks ..............................................................................77
3.4. Network Operation ...........................................................................78
3.5. Operation of The Layers ....................................................................79
3.6. What Makes The Different Neurocomputation? .................................79
3.7. Pattern Recognition ...........................................................................81
3.8. Power Synthesis ................................................................................82
3.9. Frank Rosenblatt’s Perceptron ...........................................................82
3.10. Backpropagation .............................................................................84
Chapter 4 Neural Networks Applied to the Analysis of Images ...............................93
4.1. Introduction To Patterns In Image Recognition...................................96
4.2. Digital Images .................................................................................100
4.3. Applying Neural Networks ..............................................................121
Chapter 5 Image Analysis System ..........................................................................135
5.1. System Structure .............................................................................136
5.2. Analysis of The Image .....................................................................136
5.3. Architecture ....................................................................................138
5.4. Image Processing ............................................................................139
5.5. Training Process ..............................................................................145
Chapter 6 Design and Construction of A System For
Detecting Electromyographic Signals Using Neural Networks ..............151
6.1. Electrodes .......................................................................................154
6.2. Electromyography ...........................................................................154
6.3. Electronic Fundamentals .................................................................161
6.4. The Electromyograph ......................................................................166
6.5. Design And Construction Of The Prototype For The Acquisition
of Electromyographic Signals With Bipolar Source .......................170
Chapter 7 Conclusions ...........................................................................................203
Bibliography ..........................................................................................205
Index .....................................................................................................209
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