Systems Engineering Neural Networks

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.

Table of Contents:
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