Neural Networks and Intellect

Chapters 1-7, 9, and 10 end with Notes, Bibliographical Notes, and Problems Chapter 8 ends with Bibliographical Notes and Problems Chapters 11 and 12 end with Notes and Bibliographical Notes Preface PART ONE: OVERVIEW: 2300 YEARS OF PHILOSOPHY, 100 YEARS OF MATHEMATICAL LOGIC, AND 50 YEARS OF COMPUTATIONAL INTELLIGENCE 1. Introduction: Concepts of Intelligence 1.1. Concepts of Intelligence in Mathematics, Psychology, and Philosophy 1.2. Probability, Hypothesis Choice, Pattern Recognition, and Complexity 1.3. Prediction, Tracking, and Dynamic Models 1.4. Preview: Intelligence, Internal Model, Symbol, Emotions, and Consciousness 2. Mathematical Concepts of Mind 2.1. Complexity, Aristotle, and Fuzzy Logic 2.2. Nearest Neighbors and Degenerate Geometries 2.3. Gradient Learning, Back Propagation, and Feedforward Neural Networks 2.4. Rule-Based Artificial Intelligence 2.5. Concept of Internal Model 2.6. Abductive Reasoning 2.7. Statistical Learning Theory and Support Vector Machines 2.8. AI Debates Past and Future 2.9. Society of Mind 2.10. Sensor Fusion and JDL Model 2.11. Hierarchical Organization 2.12. Semiotics 2.13. Evolutionary Computation, Genetic Algorithms, and CAS 2.14. Neural Field Theories 2.15. Intelligence, Learning, and Computability 3. Mathematical versus Metaphysical Concepts of Mind 3.1. Prolegomenon: Plato, Antisthenes, and Artifical Intelligence 3.2. Learning from Aristotle to Maimonides 3.3. Heresy of Occam and Scientific Method 3.4. Mathematics vs. Physics 3.5. Kant: Pure Spirit and Psychology 3.6. Freud vs. Jung: Psychology of Philosophy 3.7. Wither We Go From Here? PART II: MODELING FIELD THEORY: NEW MATHEMATICAL THEORY OF INTELLIGENCE WITH EXAMPLES OF ENGINEERING APPLICATIONS 4. Modeling Field Theory 4.1. Internal Models, Uncertainties, and Similarities 4.2. Modeling Field Theory Dynamics 4.3. Bayesian MFT 4.4. Shannon-Einsteinian MFT 4.5. Modeling Field Theory Neural Architecture 4.6. Convergence 4.7. Learning of Structures, AIC, and SLT 4.8. Instinct of World Modeling: Knowledge Instinct 5. MLANS: Maximum Likelihood Adaptive Neural System for Grouping and Recognition 5.1. Grouping, Classification, and Models 5.2. Gaussian Mixture Model: Unsupervised Learning or Grouping 5.3. Combined Supervised and Unsupervised Learning 5.4. Structure Estimation 5.5. Wishart and Rician Mixture Models for Radar Image Classification 5.6. Convergence 5.7. MLANS, Physics, Biology, and Other Neural Networks 6. Einsteinian Neural Network 6.1. Images, Signals, and Spectra 6.2. Spectral Models 6.3. Neural Dynamics of ENN 6.4. Applications to Acoustic Transient Signals and Speech Recognition 6.5. Applications to Electromagnetic Wave Propagation in the Ionosphere 6.6. Summary 6.7. Appendix 7. Prediction, Tracking, and Dynamic Models 7.1. Prediction, Association, and Nonlinear Regression 7.2. Association and Tracking Using Bayesian MFT 7.3. Association and Tracking Using Shannon-Einsteinian MFT (SE-CAT) 7.4. Sensor Fusion MFT 7.5.