Applied Neural Networks and Soft Computing Ivan Stanimirović Arcler Press 2010 Winston Park Drive, 2nd Floor Oakville, ON L6H 5R7 Canada www.arclerpress.com Tel: 001-289-291-7705 001-905-616-2116 Fax: 001-289-291-7601 Email: [email protected] e-book Edition 2019 ISBN: 978-1-77361-586-8 (e-book) This book contains information obtained from highly regarded resources. Reprinted material sources are indicated and copyright remains with the original owners. Copyright for images and other graphics remains with the original owners as indicated. A Wide variety of references are listed. Reasonable effortshave been made to publish reliable data. Authors or Editors or Publish- ers are not responsible for the accuracy of the information in the published chapters or conse- quences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The authors or editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify.
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 viii.