상세정보

SYSTEMS ENGINEERING NEURAL NETWORKS

A complete and authoritative discussion of systems engineering and neural networks

In Systems Engineering Neural Networks, a team of distinguished researchers deliver a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems engineering mindset. In the book, you’ll find a general theoretical discussion of both systems engineering and neural networks accompanied by coverage of relevant and specific topics, from deep learning fundamentals to sport business applications.

Readers will discover in-depth examples derived from many years of engineering experience, a comprehensive glossary with links to further reading, and supplementary online content. The authors have also included a variety of applications programmed in both Python 3 and Microsoft Excel.

The book provides:

• A thorough introduction to neural networks, introduced as key element of complex systems
• Practical discussions of systems engineering and forecasting, complexity theory and optimization and how these techniques can be used to support applications outside of the traditional AI domains
• Comprehensive explorations of input and output, hidden layers, and bias in neural networks, as well as activation functions, cost functions, and back-propagation
• Guidelines for software development incorporating neural networks with a systems engineering methodology

Perfect for students and professionals eager to incorporate machine learning techniques into their products and processes, Systems Engineering Neural Networks will also earn a place in the libraries of managers and researchers working in areas involving neural networks.

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A complete and authoritative discussion of systems engineering and neural networks

In Systems Engineering Neural Networks, a team of distinguished researchers deliver a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems engineering mindset. In the book, you’ll find a general theoretical discussion of both systems engineering and neural networks accompanied by coverage of relevant and specific topics, from deep learning fundamentals to sport business applications.

Readers will discover in-depth examples derived from many years of engineering experience, a comprehensive glossary with links to further reading, and supplementary online content. The authors have also included a variety of applications programmed in both Python 3 and Microsoft Excel.

The book provides:

• A thorough introduction to neural networks, introduced as key element of complex systems
• Practical discussions of systems engineering and forecasting, complexity theory and optimization and how these techniques can be used to support applications outside of the traditional AI domains
• Comprehensive explorations of input and output, hidden layers, and bias in neural networks, as well as activation functions, cost functions, and back-propagation
• Guidelines for software development incorporating neural networks with a systems engineering methodology

Perfect for students and professionals eager to incorporate machine learning techniques into their products and processes, Systems Engineering Neural Networks will also earn a place in the libraries of managers and researchers working in areas involving neural networks.

정보제공 :

ABOUT THE AUTHORS
ACKNOWLEDGEMENTS 7

HOW TO READ THIS BOOK 8

Part I 9

1 A BRIEF INTRODUCTION 9

THE SYSTEMS ENGINEERING APPROACH TO ARTIFICIAL INTELLIGENCE (AI) 14

SOURCES 18

CHAPTER SUMMARY 18

QUESTIONS 19

2 DEFINING A NEURAL NETWORK 20

BIOLOGICAL NETWORKS 22

FROM BIOLOGY TO MATHEMATICS 24

WE CAME A FULL CIRCLE 25

THE MODEL OF McCULLOCH-PITTS 25

THE ARTIFICIAL NEURON OF ROSENBLATT 26

FINAL REMARKS 33

SOURCES 35

CHAPTER SUMMARY 36

QUESTIONS 37

3 ENGINEERING NEURAL NETWORKS 38

A BRIEF RECAP ON SYSTEMS ENGINEERING 40

THE KEYSTONE: SE4AI AND AI4SE 41

ENGINEERING COMPLEXITY 41

THE SPORT SYSTEM 45

ENGINEERING A SPORT CLUB 51

OPTIMISATION 52

AN EXAMPLE OF DECISION MAKING 56

FUTURISM AND FORESIGHT 60

QUALITATIVE TO QUANTITATIVE 61

FUZZY THINKING 64

IT IS ALL IN THE TOOLS 74

SOURCES 77

CHAPTER SUMMARY 77

QUESTIONS 78

Part II 79

4 SYSTEMS THINKING FOR SOFTWARE DEVELOPMENT 79

PROGRAMMING LANGUAGES 82

ONE MORE THING: SOFTWARE ENGINEERING 94

CHAPTER SUMMARY 101

QUESTIONS 102

SOURCES 102

5 PRACTICE MAKES PERFECT 103

EXAMPLE 1: COSINE FUNCTION 105

EXAMPLE 2: CORROSION ON A METAL STRUCTURE 112

EXAMPLE 3: DEFINING ROLES OF ATHLETES 127

EXAMPLE 4: ATHLETE’S PERFORMANCE 134

EXAMPLE 5: TEAM PERFORMANCE 142

A human-defined-system 142

Human Factors 143

The sport team as system of interest 144

Impact of Human Error on Sports Team Performance 145

EXAMPLE 6: TREND PREDICTION 156

EXAMPLE 7: SYMPLEX AND GAME THEORY 163

EXAMPLE 8: SORTING MACHINE FOR LEGO® BRICKS 168

Part III 174

6 INPUT/OUTPUT, HIDDEN LAYER AND BIAS 174

INPUT/OUTPUT 175

HIDDEN LAYER 180

BIAS 184

FINAL REMARKS 186

CHAPTER SUMMARY 187

QUESTIONS 188

7 ACTIVATION FUNCTION 189

TYPES OF ACTIVATION FUNCTIONS 191

ACTIVATION FUNCTION DERIVATIVES 194

ACTIVATION FUNCTIONS RESPONSE TO W AND b VARIABLES 200

FINAL REMARKS 202

CHAPTER SUMMARY 204

QUESTIONS 205

SOURCES 205

8 COST FUNCTION, BACK-PROPAGATION AND OTHER ITERATIVE METHODS 206

WHAT IS THE DIFFERENCE BETWEEN LOSS AND COST? 209

TRAINING THE NEURAL NETWORK 212

BACK-PROPAGATION (BP) 214

ONE MORE THING: GRADIENT METHOD AND CONJUGATE GRADIENT METHOD 218

ONE MORE THING: NEWTON’S METHOD 221

CHAPTER SUMMARY 223

QUESTIONS 224

SOURCES 224

9 CONCLUSIONS AND FUTURE DEVELOPMENTS 225

GLOSSARY AND INSIGHTS 233