Neural Networks and Intellect: Using Model-Based Concepts / Edition 1

Neural Networks and Intellect: Using Model-Based Concepts / Edition 1

by Leonid I. Perlovsky
     
 

ISBN-10: 0195111621

ISBN-13: 9780195111620

Pub. Date: 10/19/2000

Publisher: Oxford University Press, USA

Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique text discusses deep

Overview

Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique text discusses deep philosophical questions in detail and relates them to mathematics and the engineering of intelligence. Ideal for courses in neural networks, modern pattern recognition, and mathematical concepts of intelligence, it will also be of interest to anyone working in a variety of fields including neural networks, AI, cognitive science, fuzzy systems, pattern recognition and machine/computer vision, data mining, robotics, target tracking, and financial forecasting.
Neural Networks and Intellect describes model-based neural networks that utilize the intriguing concept of an internal "world" model, an idea that originated in artificial intelligence and cognitive psychology but whose roots date back to Plato and Aristotle. Combining the a priori knowledge with adaptive learning, the new mathematical concept addresses the most perplexing problems in the field of neural networks: fast learning and robust generalization. The author provides an overview of computational intelligence and neural networks, relating hundreds of seemingly disparate techniques to several fundamental mathematical concepts, which are in turn linked to concepts of mind in philosophy, psychology, and linguistics. Topics covered include the hierarchical and heterarchical organization of intelligent systems, statistical learning theory, genetic algorithms, complex adaptive systems, mathematical semiotics, the dynamic nature of symbols, Godel theorems and intelligence, emotions and thinking, the mathematics of emotional intellect, consciousness, and more. Perlovsky's remarkable conclusion is that the work of ancient philosophers came closer to the computational concepts emerging today than that of pattern recognition and AI experts of just a few years ago.
The following website contains information about Dr. Perlovsky's current research related to the theory developed in the book and about available funding opportunities under a Research Associateship Program: to find it search for Perlovsky on http://www4/nationalacademies.org/pga/rap.nsf. Other sources of funding might be available for US-based and international researchers.

Product Details

ISBN-13:
9780195111620
Publisher:
Oxford University Press, USA
Publication date:
10/19/2000
Edition description:
New Edition
Pages:
496
Product dimensions:
9.10(w) x 7.40(h) x 1.10(d)

Table of Contents

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. Attention
8. Quantum Modeling Field Theory (QMFT)
8.1. Quantum Computing and Quantum Physics Notations
8.2. Gibbs Quantum Modeling Field System
8.3. Hamiltonian Quantum Modeling Field System
9. Fundamental Limitations on Learning
9.1. The Cramer-Rao Bound on Speed of Learning
9.2. Overlap Between Classes
9.3. CRB for MLANS
9.4. CRB for Concurrent Association and Tracking (CAT)
9.5. Summary: CRB for Intellect and Evolution?
9.6. Appendix: CRB Rule of Thumb for Tracking
10. Intelligent Systems Organization: MFT, Genetic Algorithms, and Kant
10.1. Kant, MFT, and Intelligent Systems
10.2. Emotional Machine (Toward Mathematics of Beauty)
10.3. Learning: Genetic Algorithms, MFT, and Semiosis
PART THREE: FUTURISTIC DIRECTIONS: FUN STUFF: MIND—PHYSICS + MATHEMATICS + CONJECTURES
11. Godel's Theorems, Mind, and Machine
11.1. Penrose and Computability of Mathematical Understanding
11.2. Logic and Mind
11.3. Godel, Turing, Penrose, and Putnam
11.4. Godel Theorem vs. Physics of Mind
12. Toward Physics of Consciousness
12.1. Phenomenology of Consciousness
12.2. Physics of Spiritual Substance: Future Directions
12.3. Epilogue
List of Symbols
Definitions
Bibliography
Index

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