Handbook of mathematical and digital engineering foundations for artificial intelligence: a systems methodology
Artificial intelligence and digital engineering have become prevalent in business, industry, government, and academia. However, the workforce still has a lot to learn. This handbook presents the preparatory and operational foundations for the efficacy, applicability, risk, and how to take advantage...
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2023
|
| Edition: | First edition |
| Series: | Systems innovation book series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | Artificial intelligence and digital engineering have become prevalent in business, industry, government, and academia. However, the workforce still has a lot to learn. This handbook presents the preparatory and operational foundations for the efficacy, applicability, risk, and how to take advantage of these tools and techniques |
| Item Description: | 1. Artificial Intelligence within Industrial and Systems Engineering Framework. 2. Mathematics of Cantor Set for AI Searches. 3. Set-theoretic Systems for AI Applications. 4. AI Mathematical Modeling for Product Design. 5. Mathematical Formulation of the Pursuit Problem for AI Gaming. 6. AI Framework for the Financial Sector. 7. AI Neuro-Fuzzy Model for Healthcare Prediction. 8. Stochasticity in AI Mathematical Modeling. 9. Mathematical Utility Modeling for AI Application. 10. Artificial Intelligence and Human Factors Integration in Additive Manufacturing. 11. AI Systems Optimization Techniques. 12. Mathematical Modeling and Control of Resource Constraints. Appendix A: Mathematical Expressions and Collections (Series, Patterns, and Formulae). Appendix B: Cantor Set Sectioning |
| Physical Description: | xvii, 380 Seiten Illustrationen |
| ISBN: | 9781032161815 9781032161822 1032161817 |
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Contents Preface. xiü Acknowledgments. xv Authors. xvii Chapter 1 Artificial Intelligence within Industrial and Systems Engineering Framework. 1 1.1 Introduction. 1 1.2 Quantum Potential for AI. 2 1.3 Old and New AI Achievements. 2 1.4 Industrial Engineering Linkage. 6 1.5 Historical Background of AI. 7 1.6 Origin of Artificial Intelligence. 8 1.7 Human Intelligence versus Machine Intelligence. 9 1.8 Natural Language Dichotomies. 11 1.9 The First Conference on Artificial Intelligence. 13 1.10 Evolution of Smart Programs. 13 1.11 Branches of Artificial Intelligence. 16 1.12 Neural Networks. 16 1.13 Emergence of Expert
Systems. 18 References. 21 Chapter 2 Mathematics of Cantor Set for AI Searches.23 Introduction. 23 Intelligent Searches. 24 Backdrop for AI Searches. 24 Mathematics of Cantor Set. 25 2.4.1 Set Sectioning Technique of Cantor Set.28 2.4.2 Search of Asymmetrically Distributed Data. 30 2.4.3 Derivation of the Mode Estimating Formula in Terms of Inclusive GraphicSkewness. 31 2.4.4 Graphical Verification of the mod%-IGS Relationship. 32 2.5 Results of Preliminary Research. 33 2.6 Cantor and Binary Search Comparison of 1500 Data Point Files.34 2.7 Cantor and Binary Search Comparison of 150 Data Point Files.35 2.8 Comparison of the Binary Search and the Cantor Search for the various Database Sizes.36 2.9 Intelligent 1/n Sectioning of the Search Space.38
References. 40 2.1 2.2 2.3 2.4 vii
Contents viii Chapter 3 Set-theoretic Systems for AI Applications. 43 Set Systems in Problem Domains. 43 Sets and Systems in Innovation. 44 Ordered Pairs on Sets. 44 Set Relations in Innovation Systems.45 Functions on Sets. 45 Cardinality of Sets. 46 Relationships of Set-to-System and Subset-to-Subsystem.46 3.8 Integration Mapping of Subsets. 48 3.9 Model Reduction Approach. 48 3.10 Singular Value Decomposition in Innovation Systems. 50 3.11 Subsystem Surface Projection Integrals. 51 3.12 Set Projection and System Overlap. 54 3.13 Time-Variant Systems Integration. 55 3.14 Modal Canonical Representation. 56 3.15 Canonical Estimation Procedure. 57 3.16 Hypothetical Example. 60 3.17
Conclusion. 60 References. 60 3.1 3.2 3.3 3.4 3.5 3.6 3.7 Chapter 4 AI Mathematical Modeling for Product Design. 63 Introduction. 63 Product Design Background. 65 Memetic Algorithm and Its Application to Collaborative Design. 67 4.4 A Framework for Collaborative Design. 67 4.5 The Pseudo Code.68 4.6 Case Example of Forearm Crutch Design. 70 4.7 Design Problem Formulation. 71 4.8 Design Agent for Strength Decision. 72 4.9 System Implementation. 73 4.10 System Results and Analysis. 74 4.11 Conclusion. 76 References. 76 4.1 4.2 4.3 Chapter 5 Mathematical Formulation of the Pursuit Problem for AI
Gaming. 79 5.1 Introduction to the Pursuit Problem. 79 5.2 Introduction. 80 5.3 The Classical Approach. 80 5.4 The Intercept Approach. 83 5.5 Example. 86 5.6 Conclusion. 90 References. 90
Contents Chapter 6 ix AI Framework for the Financial Sector. 91 6.1 Introduction. 91 6.2 Methodology. 92 6.3 Discussion. 94 6.4 Conclusion. 95 References. 95 Chapter 7 AI Neuro-Fuzzy Model for Healthcare Prediction.97 Introduction. 97 Coupled Insulin and Meal Effect Neuro-Fuzzy Network Model. 99 7.2.1 Model Assumptions. 99 7.2.2 Model Formulation. 100 7.2.3 Blood Glucose Level (BGL), x(k). 100 7.2.4 Insulin Injection, u{. 100 7.2.5 Meal Intake, u2. 101 7.3 Fuzzification of State and Input Variables.101 7.3.1 Formulation of the T-SModel Rule Bases. 103 7.4 Parameter Identification.108 7.5 Prediction of Blood Glucose
Level.108 7.6 Choice of Training Scenario.109 7.7 Results, Observations and Discussions. 109 7.8 Conclusion. Ill 7.9 Contributions to Knowledge.113 References. 113 7.1 7.2 Chapter 8 Stochasticity in AI Mathematical Modeling. 115 8.1 8.2 8.3 8.4 8.5 Introduction. 115 The Material/Iconic Models. 115 AI Mathematical Models. 115 Systems Filtering and Estimation.119 8.4.1 Identification. 119 Correlation Techniques. 120 8.5.1 System Estimation. 122 8.5.2 Problem Formulation. 122 8.5.3 Maximum Likelihood. 123 8.5.4 Bayes Estimators. 124 8.5.5 Minimum Variance. 124 8.5.6 For Linear Minimum Variance Unbiased (Gauss-
Markov). 124 8.5.7 Partitioned Data Sets. 126 8.5.8 Kalman Form. 127 8.5.8.1 Discrete Dynamic Linear System Estimation. 128
Contents X Prediction. 128 Filtering. 128 Smoothing. 129 8.5.11.1 Continuous Dynamic Linear System.129 8.6 Continuous Nonlinear Estimation. 131 8.7 Extended Kalman Filter. 133 8.7.1 Partitional Estimation. 134 8.8 Invariant Imbedding.135 8.9 Stochastic Approximations/Innovations Concept. 135 8.10 Model Control - Model Reduction, Model Analysis. 137 8.10.1 Introduction. 137 8.11 Modal Approach for Estimation in Distributed Parameter Systems. 140 8.12 The Modal Canonical Representation. 141 References. 147 8.5.9 8.5.10 8.5.11 Chapter 9 Mathematical Utility Modeling for AI Application.149 Introduction to Utility Modeling. 149 Innovation Investment Challenge. 149 Utility Models. 149
9.3.1 Additive Utility Model. 152 9.3.2 Multiplicative Utility Model. 153 9.3.3 Fitting a Utility Function. 153 9.3.4 Investment Value Model.159 9.4 Capability. 160 9.5 Suitability. 160 9.6 Performance. 160 9.7 Productivity. 161 9.8 Polar Plots. 161 9.9 Technical Innovation Benchmarking.168 References. 170 9.1 9.2 9.3 Chapter 10 Artificial Intelligence and Human Factors Integration in Additive Manufacturing. 171 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 Introduction. 171 Background of Additive Manufacturing. 171 Cognitive Ergonomics. 173 Computational Methods. 174 Human Considerations in Additive
Manufacturing. 177 Artificial Intelligence Case Studies. 180 Cognitive Ergonomics in Additive Manufacturing.181 Human-Machine Integration. 183 10.8.1 Implementation Strategy for DEJI Model in Human Factors for Additive Manufacturing.186
xi Contents 10.9 Conclusion. 188 References. 188 Chapter 11 AI Systems Optimization Techniques. 193 Introduction. 193 Basic Structure of Local Methods. 198 Descent Directions. 198 Steepest Descent (SD). 199 Conjugate Gradient (CG). 199 Newton Methods. 199 11.6.1 Algorithm: Modified Newton. 200 11.7 The Stochastic Central Problems. 200 11.7.1 Stochastic Approximation. 201 11.7.2 General Stochastic Control Problem.202 11.8 The Intelligent Heuristic Models. 203 11.8.1 Heuristics. 204 11.8.2 Intelligent Systems.204 11.8.3 The General Search Paradigm. 204 11.8.3.1 General Search.204 11.8.4 Integrated Heuristics. 205 11.8.5
TabuSearch.205 11.8.5.1 TabuSearch .205 11.8.6 Simulated Annealing (SA).206 11.8.6.1 Simulated Annealing.206 11.8.7 Genetic Algorithms (GA).207 11.8.7.1 Genetic Algorithm (11.7).208 11.8.7.2 Procedure GA.208 11.8.8 Genetic Algorithm Operators.208 11.8.8.1 Examples.209 11.8.8.2 Applications of Heuristics to Intelligent Systems. 211 11.8.8.3 High-Performance Optimization Programming.212 References. 213 11.1 11.2 11.3 11.4 11.5 11.6 Chapter 12 Mathematical Modeling and Control of Resource Constraints. 215 Introduction. 215 The Nature of Resource Constraints. 215 Literature Background. 216 Methodology. 217 Mathematical Notations. 217 Representation of Resource Interdependencies and
Multifunctionality. 218 12.7 Modeling of Resource Characteristics. 220 12.1 12.2 12.3 12.4 12.5 12.6
Contents xii 12.8 Resource Mapper. 223 12.9 Activity Scheduler. 224 12.10 Model Implementation andGraphical Illustrations. 233 References. 236 Appendix A Mathematical Expressions and Collections (Series, Patterns, and Formulae). 237 Appendix В Cantor Set Sectioning. 371 Index. 379 |
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| spelling | Badiru, Adedeji Bodunde 1952- Verfasser (DE-588)124156665 aut Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology Adedeji B. Badiru, Olumuyiwa Asaolu First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2023 xvii, 380 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Systems innovation book series 1. Artificial Intelligence within Industrial and Systems Engineering Framework. 2. Mathematics of Cantor Set for AI Searches. 3. Set-theoretic Systems for AI Applications. 4. AI Mathematical Modeling for Product Design. 5. Mathematical Formulation of the Pursuit Problem for AI Gaming. 6. AI Framework for the Financial Sector. 7. AI Neuro-Fuzzy Model for Healthcare Prediction. 8. Stochasticity in AI Mathematical Modeling. 9. Mathematical Utility Modeling for AI Application. 10. Artificial Intelligence and Human Factors Integration in Additive Manufacturing. 11. AI Systems Optimization Techniques. 12. Mathematical Modeling and Control of Resource Constraints. Appendix A: Mathematical Expressions and Collections (Series, Patterns, and Formulae). Appendix B: Cantor Set Sectioning Artificial intelligence and digital engineering have become prevalent in business, industry, government, and academia. However, the workforce still has a lot to learn. This handbook presents the preparatory and operational foundations for the efficacy, applicability, risk, and how to take advantage of these tools and techniques Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Artificial intelligence Automatic control engineering COMPUTERS / Artificial Intelligence COMPUTERS / Programming / Systems Analysis & Design DES011000 Electrical engineering Engineering: general Environmental science, engineering & technology Ergonomics Ergonomie Ingenieurswesen, Maschinenbau allgemein Künstliche Intelligenz Mathematical modelling Other manufacturing technologies Product design Produktdesign Regelungstechnik Systemanalyse und -design Systems analysis & design TECHNOLOGY & ENGINEERING / Automation Maschinelles Lernen (DE-588)4193754-5 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Asaolu, Olumuyiwa Sunday Verfasser (DE-588)1219584886 aut Erscheint auch als Online-Ausgabe 978-1-003-24739-5 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034610229&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Badiru, Adedeji Bodunde 1952- Asaolu, Olumuyiwa Sunday Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology Mathematisches Modell (DE-588)4114528-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology |
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| title_exact_search | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology |
| title_exact_search_txtP | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology |
| title_full | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology Adedeji B. Badiru, Olumuyiwa Asaolu |
| title_fullStr | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology Adedeji B. Badiru, Olumuyiwa Asaolu |
| title_full_unstemmed | Handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology Adedeji B. Badiru, Olumuyiwa Asaolu |
| title_short | Handbook of mathematical and digital engineering foundations for artificial intelligence |
| title_sort | handbook of mathematical and digital engineering foundations for artificial intelligence a systems methodology |
| title_sub | a systems methodology |
| topic | Mathematisches Modell (DE-588)4114528-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Mathematisches Modell Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034610229&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT badiruadedejibodunde handbookofmathematicalanddigitalengineeringfoundationsforartificialintelligenceasystemsmethodology AT asaoluolumuyiwasunday handbookofmathematicalanddigitalengineeringfoundationsforartificialintelligenceasystemsmethodology |