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ویرایش: [1 ed.] نویسندگان: Adedeji B. Badiru, Olumuyiwa Asaolu سری: ISBN (شابک) : 1032161817, 9781032161815 ناشر: CRC Press سال نشر: 2023 تعداد صفحات: 398 [399] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 14 Mb
در صورت تبدیل فایل کتاب Handbook of Mathematical and Digital Engineering Foundations for Artificial Intelligence: A Systems Methodology (Systems Innovation Book Series) به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کتاب مبانی مهندسی ریاضی و دیجیتال برای هوش مصنوعی: روش شناسی سیستمی (سری کتاب های نوآوری سیستم) نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
راهنمای استفاده از پلتفرمهای مهندسی دیجیتال برای پیشرفت برنامههای هوش مصنوعی را ارائه میدهد. بحث در مورد رابط آموزش و تحقیق در تعقیب پیشرفتهای هوش مصنوعی ارائه یکپارچگی از مهارتهای نرم و سخت در توسعه و استفاده از هوش مصنوعی. تسهیل آموزش پیشرفته از طریق هوش مصنوعی و سیستم های مهندسی دیجیتال
Provides a guide for using digital engineering platforms for advancing AI applications Discusses an interface of education and research in the pursuit of AI developments Presents an integration of soft and hard skills in developing and using AI Offers a rigorous systems approach to understanding and using AI Highlights the facilitation of advanced education through AI and digital engineering systems
Cover Half Title Series Page Title Page Copyright Page Dedication Table of Contents Preface Acknowledgments Authors Chapter 1: Artificial Intelligence within Industrial and Systems Engineering Framework 1.1 Introduction 1.2 Quantum Potential for AI 1.3 Old and New AI Achievements 1.4 Industrial Engineering Linkage 1.5 Historical Background of AI 1.6 Origin of Artificial Intelligence 1.7 Human Intelligence versus Machine Intelligence 1.8 Natural Language Dichotomies 1.9 The First Conference on Artificial Intelligence 1.10 Evolution of Smart Programs 1.11 Branches of Artificial Intelligence 1.12 Neural Networks 1.13 Emergence of Expert Systems References Chapter 2: Mathematics of Cantor Set for AI Searches 2.1 Introduction 2.2 Intelligent Searches 2.3 Backdrop for AI Searches 2.4 Mathematics of Cantor Set 2.4.1 Set Sectioning Technique of Cantor Set 2.4.2 Search of Asymmetrically Distributed Data 2.4.3 Derivation of the Mode Estimating Formula in Terms of Inclusive Graphic Skewness 2.4.4 Graphical Verification of the mod%–IGS Relationship 2.5 Results of Preliminary Research 2.6 Cantor and Binary Search Comparison of 1500 Data Point Files 2.7 Cantor and Binary Search Comparison of 150 Data Point Files 2.8 Comparison of the Binary Search and the Cantor Search for the various Database Sizes 2.9 Intelligent 1/n Sectioning of the Search Space References Chapter 3: Set-theoretic Systems for AI Applications 3.1 Set Systems in Problem Domains 3.2 Sets and Systems in Innovation 3.3 Ordered Pairs on Sets 3.4 Set Relations in Innovation Systems 3.5 Functions on Sets 3.6 Cardinality of Sets 3.7 Relationships of Set-to-System and Subset-to-Subsystem 3.8 Integration Mapping of Subsets 3.9 Model Reduction Approach 3.10 Singular Value Decomposition in Innovation Systems 3.11 Subsystem Surface Projection Integrals 3.12 Set Projection and System Overlap 3.13 Time-Variant Systems Integration 3.14 Modal Canonical Representation 3.15 Canonical Estimation Procedure 3.16 Hypothetical Example 3.17 Conclusion References Chapter 4: AI Mathematical Modeling for Product Design 4.1 Introduction 4.2 Product Design Background 4.3 Memetic Algorithm and Its Application to Collaborative Design 4.4 A Framework for Collaborative Design 4.5 The Pseudo Code 4.6 Case Example of Forearm Crutch Design 4.7 Design Problem Formulation 4.8 Design Agent for Strength Decision 4.9 System Implementation 4.10 System Results and Analysis 4.11 Conclusion References Chapter 5: Mathematical Formulation of the Pursuit Problem for AI Gaming 5.1 Introduction to the Pursuit Problem 5.2 Introduction 5.3 The Classical Approach 5.4 The Intercept Approach 5.5 Example 5.6 Conclusion References Chapter 6: AI Framework for the Financial Sector 6.1 Introduction 6.2 Methodology 6.3 Discussion 6.4 Conclusion References Chapter 7: AI Neuro-Fuzzy Model for Healthcare Prediction 7.1 Introduction 7.2 Coupled Insulin and Meal Effect Neuro-Fuzzy Network Model 7.2.1 Model Assumptions 7.2.2 Model Formulation 7.2.3 Blood Glucose Level (BGL), x ( k) 7.2.4 Insulin Injection, u 1 7.2.5 Meal Intake, u 2 7.3 Fuzzification of State and Input Variables 7.3.1 Formulation of the T-S Model Rule Bases 7.4 Parameter Identification 7.5 Prediction of Blood Glucose Level 7.6 Choice of Training Scenario 7.7 Results, Observations and Discussions 7.8 Conclusion 7.9 Contributions to Knowledge References Chapter 8: Stochasticity in AI Mathematical Modeling 8.1 Introduction 8.2 The Material/Iconic Models 8.3 AI Mathematical Models 8.4 Systems Filtering and Estimation 8.4.1 Identification 8.5 Correlation Techniques 8.5.1 System Estimation 8.5.2 Problem Formulation 8.5.3 Maximum Likelihood 8.5.4 Bayes Estimators 8.5.5 Minimum Variance 8.5.6 For Linear Minimum Variance Unbiased (Gauss-Markov) 8.5.7 Partitioned Data Sets 8.5.8 Kalman Form 8.5.8.1 Discrete Dynamic Linear System Estimation 8.5.9 Prediction 8.5.10 Filtering 8.5.11 Smoothing 8.5.11.1 Continuous Dynamic Linear System 8.6 Continuous Nonlinear Estimation 8.7 Extended Kalman Filter 8.7.1 Partitional Estimation 8.8 Invariant Imbedding 8.9 Stochastic Approximations/Innovations Concept 8.10 Model Control – Model Reduction, Model Analysis 8.10.1 Introduction 8.11 Modal Approach for Estimation in Distributed Parameter Systems 8.12 The Modal Canonical Representation References Chapter 9: Mathematical Utility Modeling for AI Application 9.1 Introduction to Utility Modeling 9.2 Innovation Investment Challenge 9.3 Utility Models 9.3.1 Additive Utility Model 9.3.2 Multiplicative Utility Model 9.3.3 Fitting a Utility Function 9.3.4 Investment Value Model 9.4 Capability 9.5 Suitability 9.6 Performance 9.7 Productivity 9.8 Polar Plots 9.9 Technical Innovation Benchmarking References Chapter 10: Artificial Intelligence and Human Factors Integration in Additive Manufacturing 10.1 Introduction 10.2 Background of Additive Manufacturing 10.3 Cognitive Ergonomics 10.4 Computational Methods 10.5 Human Considerations in Additive Manufacturing 10.6 Artificial Intelligence Case Studies 10.7 Cognitive Ergonomics in Additive Manufacturing 10.8 Human–Machine Integration 10.8.1 Implementation Strategy for DEJI Model in Human Factors for Additive Manufacturing 10.9 Conclusion References Chapter 11: AI Systems Optimization Techniques 11.1 Introduction 11.2 Basic Structure of Local Methods 11.3 Descent Directions 11.4 Steepest Descent (SD) 11.5 Conjugate Gradient (CG) 11.6 Newton Methods 11.6.1 Algorithm: Modified Newton 11.7 The Stochastic Central Problems 11.7.1 Stochastic Approximation 11.7.2 General Stochastic Control Problem 11.8 The Intelligent Heuristic Models 11.8.1 Heuristics 11.8.2 Intelligent Systems 11.8.3 The General Search Paradigm 11.8.3.1 General Search 11.8.4 Integrated Heuristics 11.8.5 Tabu Search 11.8.5.1 Tabu Search 11.8.6 Simulated Annealing (SA) 11.8.6.1 Simulated Annealing 11.8.7 Genetic Algorithms (GA) 11.8.7.1 Genetic Algorithm (11.7) 11.8.7.2 Procedure GA 11.8.8 Genetic Algorithm Operators 11.8.8.1 Examples 11.8.8.2 Applications of Heuristics to Intelligent Systems 11.8.8.3 High-Performance Optimization Programming References Chapter 12: Mathematical Modeling and Control of Resource Constraints 12.1 Introduction 12.2 The Nature of Resource Constraints 12.3 Literature Background 12.4 Methodology 12.5 Mathematical Notations 12.6 Representation of Resource Interdependencies and Multifunctionality 12.7 Modeling of Resource Characteristics 12.8 Resource Mapper 12.9 Activity Scheduler 12.10 Model Implementation and Graphical Illustrations References Appendix A: Mathematical Expressions and Collections (Series, Patterns, and Formulae) Greek Alphabet Number Sequence and Patterns Closed Form Mathematical Expressions Derivation of the Quadratic Formula Mathematical Signs and Symbols Overall Mean Definition of Set and Notation Set Terms and Symbols Operations on Sets Probability Terminology Basic Probability Principles Random Variable Mean Value x or Expected Value μ Series Expansions Algebra Laws of Algebraic Operations Special Products and Factors Powers and Roots Proportion Arithmetic Mean of n Quantities A Geometric Mean of n Quantities G Harmonic Mean of n Quantities H Generalized Mean Solution of Quadratic Equations Solution of Cubic Equations Trigonometric Solution of the Cubic Equation Partial Fractions Non-Repeated Linear Factors Repeated Linear Factors General Terms Repeated Linear Factors Factors of Higher Degree Geometry Triangles Right Triangle Equilateral Triangle General Triangle Quadrilaterals Rectangle Parallelogram Rhombus Trapezoid General Quadrilateral Cyclic Quadrilateral Planar Areas by Approximation Solids Bounded by Planes Cube Prism Pyramid Prismatoid Regular Polyhedra Spherical Triangle and Polygon Oblate Spheroid Prolate Spheroid Circular Torus Equation of Line Joining Two Points Equation of Line in Terms of x Intercept a ≠ 0 and y intercept b ≠ 0 Normal Form for Equation of Line General Equation of Line Area of Triangle with Vertices Transformation of Coordinates Involving Pure Translation Transformation of Coordinates Involving Pure Rotation Transformation of Coordinates Involving Translation and Rotation Polar Coordinates ( r, θ) Plane Curves Catenary, Hyperbolic Cosine Cardioid Circle Cassinian Curves Cotangent Curve Cubical Parabola Cosecant Curve Cosine Curve Ellipse Gamma Function Hyperbolic Functions Inverse Cosine Curve Inverse Sine Curve Inverse Tangent Curve Logarithmic Curve Parabola Cubical Parabola Tangent Curve Ellipsoid Elliptic Cone Elliptic Cylinder Hyperboloid of One Sheet Elliptic Paraboloid Hyperboloid of Two Sheets Hyperbolic Paraboloid Sphere Logarithmic Identities Series Expansions Limiting Values Inequalities Continued Fractions Polynomial Approximations Exponential Function Series Expansion Fundamental Properties Definition of General Powers Slopes Trigonometric ratios Sine Law Cosine Law Expansions Factoring Roots of Quadratic Law of exponents Logarithms Six Simple Machines for Materials Handling Machine 1: The Lever Machine 2: Wheel and Axie Machine 3: The Pulley Machine 4: The Inclined Plane Machine 5: The Wedge Machine 6: The Screw Mechanics: Kinematics Scalars and Vectors Distance and Displacement Acceleration Speed and Velocity Frequency Period Angular Displacement Angular Velocity Angular Acceleration Rotational Speed Uniform Linear Motion Uniform Accelerated Linear Motion Rotational Motion Uniform Rotation and a Fixed Axis Uniform Accelerated Rotation about a Fixed Axis Simple Harmonic Motion Pendulum Free Fall Vertical Project Angled Projections Horizontal Projection: ( α = 0) Sliding Motion on an Inclined Plane Rolling Motion on an Inclined Plane Mechanics: Dynamics Newton’s First Law of Motion Newton’s Second Law Newton’s Third Law Momentum of Force Impulse of Force Law of Conservation of Momentum Friction General Law of Gravity Gravitational Force Centrifugal Force Centripetal Force Torque Work Energy Conservation of Energy Power Common Statistical Distributions Discrete Distributions Bernoulli Distribution Beta Binomial Distribution Beta Pascal Distribution Binomial Distribution Discrete Weibull Distribution Geometric Distribution Hypergeometric Distribution Negative Binomial Distribution Poisson Distribution Rectangular (Discrete Uniform) Distribution Continuous Distributions Arcsine Distribution Beta Distribution Cauchy Distribution Chi Distribution Chi-Square Distribution Erlang Distribution Exponential Distribution Extreme-Value Distribution F Distribution Gamma Distribution Half-Normal Distribution LaPlace (Double Exponential) Distribution Logistic Distribution Lognormal Distribution Noncentral Chi-Square Distribution Noncentral F Distribution Noncentral t Distribution Normal Distribution Pareto Distribution Rayleigh Distribution Triangular Distribution Uniform Distribution Weibull Distribution Distribution Parameters Average Variance Standard Deviation Standard Error Skewness Standardized Skewness Kurtosis Standardized Kurtosis Weighted Average Estimation and Testing Distribution Functions−Parameter Estimation Bernoulli Binomial Discrete Uniform Geometric Negative Binomial Poisson Beta Chi-Square Erlang Exponential F Gamma Log Normal Normal Student’s t Triangular Uniform Weibull Chi-square Test for Distribution Fitting Kolmogorov − Smirnov Test ANOVA Notations Standard Error (internal) Standard Error (pooled) Interval Estimates Tukey Interval Scheffe Interval Cochran C-Test Bartlett Test Hartley’s Test Kruskal-Wallis Test Freidman test Regression Notation Regression Statistics Predictions Nonlinear Regression Ridge Regression Quality Control Subgroup Statistics X Bar Charts Capability Ratios R Charts S Charts C Charts U Charts P Charts NP Charts CuSum Chart for the Mean Multivariate Control Charts Time Series Analysis Notation Autocorrelation at lag k Partial autocorrelation at lag k Cross Correlation at lag k Box-Cox Periodogram (computed using Fast Fourier Transform) Categorical Analysis Notation Totals Chi-Square Fisher’s Exact Test Lambda Uncertainty Coefficient Somer’s D Eta Contingency Coefficient Cramer’s V Conditional Gamma Pearson’s r Kendall’s Tau b Tau C Probability Terminology Basic Probability Principles Random Variable Mean Value or Expected Value μ Discrete Distribution Formulas Bernoulli Distribution Beta Binomial Distribution Beta Pascal Distribution Binomial Distribution Discrete Weibull Distribution Geometric Distribution Hypergeometric Distribution Negative Binomial Distribution Poisson Distribution Rectangular (Discrete Uniform) Distribution Continuous Distribution Formulas Arcsin Distribution Beta Distribution Cauchy Distribution Chi Distribution Chi-Square Distribution Erlang Distribution Exponential Distribution Extreme-Value Distribution F Distribution Gamma Distribution Half-Normal Distribution LaPlace (Double Exponential) Distribution Logistic Distribution Lognormal Distribution Noncentral Chi-Square Distribution Noncentral F Distribution Noncentral t Distribution Normal Distribution Pareto Distribution Rayleigh Distribution t -Distribution Triangular Distribution Uniform Distribution Weibull Distribution Variate Generation Techniques Generation Algorithms Formulas for Generating Event Times from a Renewal or Nonhomogeneous Poisson Process Example Appendix B: Cantor Set Sectioning Application of the Cantor Set Approach Comments on the Search Procedure Modified Search Procedure Alternate Search Preference Index