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.