Applied Neural Networks and Soft Computing

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Applied Neural Networks and Soft Computing examines the relation between neural networks and soft computing. Neural network is a system of hardware and software designed after the operations of neurons. Applied neural networks has a plethora of applications and the text tries to touch every aspect t
APPLIED NEURAL NETWORKS AND SOFT COMPUTING APPLIED NEURAL NETWORKS AND SOFT COMPUTING Ivan Stanimirovic ARcHer Applied neural Networks and soft Computing Ivan stanimirovic Archer press 2010 Winston Park Drive, 2nd floor Oakville. ON L6H 5R7 C anada Tel:001-289-291-7705 001-905-616-2116 Fax:001-289-291-7601 Email: orders(aarclereducation com e-book edition 2019 ISBN:978-1-77361-586-8(e-b0ok) 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 efforts have 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 rectif Notice: Registered trademark of products or corporate names are used only for explanation and identification without intent of infringement ◎2019 Archer press ISBN:978-1-77361-385-7( Hardcover) Arcler Press publishes wide variety of books and eBooks. For more information about ABOUT THE AUTHOR Ivan Stanimirovic gained his phd from University of Nis, 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 nis on computing generalized matrix inverses and its applications. TABLE OF CONTENTS List of Figures List of tables Preface Chapter 1 Introduction.....................1 1.1. Differences Between The Brain and A Computer 1.2. Artificial Neural Networks 1.3. Definition and Characteristics 4777 1.4. Processing Stages…… 1.5. Training or Learning Chapter 2 Application of an Intelligent Hopfield Neural Networks For Face Recognition 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 26. Natural Language Processing……… 55 2.7. Network Layer: Perceptron Adaline, and madaline 59 2. 8. Backpropagation 66 2.9. Validation Chapter 3 Artificial Neural Networks 75 3. 1. Introduction ·· 76 32. Analogy With The brain……… 76 33. Neural Networks 3. 4. Network Operation 78 3.5. Operation of The Layers 79 3.6. What Makes The Different Neurocomputation? 3.7. Pattern Recognition 81 3.8 Power Synthesis 82 3.9. Frank Rosenblatt's Perceptron 3.10. Backpropagation 84 Chapter4 Neural Networks Applied to the Analysis of Images……………………93 4.1. Introduction To Patterns In Image recognition 96 4.2. Digital Images 4.3. Applying Neural Networks 121 Chapter5 Image Analysis System………………………….135 5. 1. System Structure 136 5.2. Analysis of The Image......... 136 5.3. Architecture 138 5. 4. Image pr 8 139 5.5. Training Pr 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 66 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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