Neural Networks and Intellect: Using Model-Based Concepts (Hardcover)

Leonid I. Perlovsky

  • 出版商: Oxford University
  • 出版日期: 2000-10-19
  • 售價: $980
  • 貴賓價: 9.8$960
  • 語言: 英文
  • 頁數: 496
  • 裝訂: Hardcover
  • ISBN: 0195111621
  • ISBN-13: 9780195111620
  • 相關分類: 人工智慧
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商品描述

Intended for a broad audience, Neural Networks and Intellect reviews most of the mathematical concepts and engineering approaches to the development of intelligent systems discussed since 1940. It presents a new mathematical concept of modeling field theory and its applications to a variety of problems along with relationships between mathematics, computational concepts in neural networks, and concepts of mind in psychology and philosophy. The origin of the Aristotelian mathematics of mind is traced in Grossberg's ART neural network: and its essential components turns out to be fuzzy logic. Among the discussed topics are hierarchical and heterarchical organization of intelligent systems, statistical learning theory, genetic algorithms, complex adaptive systems, mathematical semiotics, dynamical nature of symbols, Godel theorems of intelligence, emotions and thinking, the mathematics of emotional intellect, and consicousness.

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

商品描述(中文翻譯)

《神經網絡與智能》是針對廣泛讀者群體的書籍,回顧了自1940年以來討論的大部分數學概念和工程方法,用於智能系統的開發。它提出了一個新的數學概念,即建模場論,以及它在各種問題上的應用,以及數學、神經網絡中的計算概念,以及心理學和哲學中的思維概念之間的關係。在Grossberg的ART神經網絡中追溯了亞里士多德心智數學的起源,其基本組件被證明是模糊邏輯。討論的主題包括智能系統的階層和非階層組織、統計學習理論、遺傳算法、複雜適應系統、數學符號學、符號的動態性質、哥德爾智能定理、情感和思維、情感智能的數學以及意識等。

目錄:
第1-7、9和10章結束時附有註釋、參考文獻註釋和問題
第8章結束時附有參考文獻註釋和問題
第11和12章結束時附有註釋和參考文獻註釋
前言
第一部分:概述:2300年的哲學、100年的數學邏輯和50年的計算智能
1. 引言:智能的概念
1.1. 數學、心理學和哲學中的智能概念
1.2. 概率、假設選擇、模式識別和複雜性
1.3. 預測、跟踪和動態模型
1.4. 預覽:智能、內部模型、符號、情感和意識
2. 心智的數學概念
2.1. 複雜性、亞里士多德和模糊邏輯
2.2. 最近鄰居和退化幾何
2.3. 梯度學習、反向傳播和前饋神經網絡
2.4. 基於規則的人工智能
2.5. 內部模型的概念
2.6. 归纳推理
2.7. 統計學習理論和支持向量機
2.8. 人工智能的過去和未來辯論
2.9. 心智的社會
2.10. 传感器融合和JDL模型
2.11. 階層組織
2.12. 符號學
2.13. 進化計算、遺傳算法和CAS
2.14. 神經場理論
2.15. 智能、學習和可計算性
3. 數學與形而上學的心智概念
3.1. 序言:柏拉圖、安提斯特尼和人工智能
3.2. 從亞里士多德到邁蒙尼德的學習
3.3. 奧卡姆的異端和科學方法
3.4. 數學與物理學
3.5. 康德:純粹精神和心理學
3.6. 弗洛伊德與荣格:哲學的心理學
3.7. 我們從這裡去哪裡?
第二部分:建模場論:智能的新數學理論及其工程應用示例
4. 建模場論
4.1. 內部模型、不確定性和相似性
4.2. 建模場論動力學
4.3. 貝葉斯建模場論
4.4. 香農-愛因斯坦建模場論
4.5. 建模場論神經架構
4.6. 收斂
4.7. 結構學習、AIC和SLT
4.8. 世界建模的本能:知識本能
5. MLANS:最大似然自適應神經系統用於分組和識別
5.1. 分組、分類和模型
5.2. 高斯混合模型:無監督學習或分組
5.3. 結合監督和無監督學習
5.4. 結構估計
5.5. 雷達圖像分類的Wishart和Rician混合模型
5.6. 收斂
5.7. MLANS、物理學、生物學和其他神經網絡
6. 愛因斯坦神經網絡
6.1. 圖像、信號和頻譜
6.2. 頻譜模型
6.3. ENN的神經動力學
6.4. 對聲學的應用