Neural Networks : a Systematic Introduction /

Artificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the underlying theory. Starting from simple thre...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rojas, Raúl (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg, 1996.
Temas:
Artificial intelligence.
Computer simulation.
Optical pattern recognition.
Computer science.
Biology > Data processing.
Digital computer simulation.
Electronic data processing.
Intelligence artificielle.
Simulation par ordinateur.
Reconnaissance optique des formes (Informatique)
Informatique.
Biologie > Informatique.
artificial intelligence.
simulation.
computer science.
data processing.
Electronic data processing
Digital computer simulation
Artificial intelligence
Biology > Data processing
Computer science
Computer simulation
Optical pattern recognition
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. The Biological Paradigm
  • 1.1 Neural computation
  • 1.2 Networks of neurons
  • 1.3 Artificial neural networks
  • 1.4 Historical and bibliographical remarks
  • 2. Threshold Logic
  • 2.1 Networks of functions
  • 2.2 Synthesis of Boolean functions
  • 2.3 Equivalent networks
  • 2.4 Recurrent networks
  • 2.5 Harmonic analysis of logical functions
  • 2.6 Historical and bibliographical remarks
  • 3. Weighted Networks
  • The Perceptron
  • 3.1 Perceptrons and parallel processing
  • 3.2 Implementation of logical functions
  • 3.3 Linearly separable functions
  • 3.4 Applications and biological analogy
  • 3.5 Historical and bibliographical remarks
  • 4. Perceptron Learning
  • 4.1 Learning algorithms for neural networks
  • 4.2 Algorithmic learning
  • 4.3 Linear programming
  • 4.4 Historical and bibliographical remarks
  • 5. Unsupervised Learning and Clustering Algorithms
  • 5.1 Competitive learning
  • 5.2 Convergence analysis
  • 5.3 Principal component analysis
  • 5.4 Some applications
  • 5.5 Historical and bibliographical remarks
  • 6. One and Two Layered Networks
  • 6.1 Structure and geometric visualization
  • 6.2 Counting regions in input and weight space
  • 6.3 Regions for two layered networks
  • 6.4 Historical and bibliographical remarks
  • 7. The Backpropagation Algorithm
  • 7.1 Learning as gradient descent
  • 7.2 General feed-forward networks
  • 7.3 The case of layered networks
  • 7.4 Recurrent networks
  • 7.5 Historical and bibliographical remarks
  • 8. Fast Learning Algorithms
  • 8.1 Introduction
  • classical backpropagation
  • 8.2 Some simple improvements to backpropagation
  • 8.3 Adaptive step algorithms
  • 8.4 Second-order algorithms
  • 8.5 Relaxation methods
  • 8.6 Historical and bibliographical remarks
  • 9. Statistics and Neural Networks
  • 9.1 Linear and nonlinear regression
  • 9.2 Multiple regression
  • 9.3 Classification networks
  • 9.4 Historical and bibliographical remarks
  • 10. The Complexity of Learning
  • 10.1 Network functions
  • 10.2 Function approximation
  • 10.3 Complexity of learning problems
  • 10.4 Historical and bibliographical remarks
  • 11. Fuzzy Logic
  • 11.1 Fuzzy sets and fuzzy logic
  • 11.2 Fuzzy inferences
  • 11.3 Control with fuzzy logic
  • 11.4 Historical and bibliographical remarks
  • 12. Associative Networks
  • 12.1 Associative pattern recognition
  • 12.2 Associative learning
  • 12.3 The capacity problem
  • 12.4 The pseudoinverse
  • 12.5 Historical and bibliographical remarks
  • 13. The Hopfield Model
  • 13.1 Synchronous and asynchronous networks
  • 13.2 Definition of Hopfield networks
  • 13.3 Converge to stable states
  • 13.4 Equivalence of Hopfield and perceptron learning
  • 13.5 Parallel combinatorics
  • 13.6 Implementation of Hopfield networks
  • 13.7 Historical and bibliographical remarks
  • 14. Stochastic Networks
  • 14.1 Variations of the Hopfield model
  • 14.2 Stochastic systems
  • 14.3 Learning algorithms and applications
  • 14.4 Historical and bibliographical remarks
  • 15. Kohonen Networks
  • 15.1 Self-organization
  • 15.2 Kohonen's model
  • 15.3 Analysis of convergence
  • 15.4 Applications
  • 15.5 Historical and bibliographical remarks
  • 16. Modular Neural Networks
  • 16.1 Constructive algorithms for modular networks
  • 16.2 Hybrid networks
  • 16.3 Historical and bibliographical remarks
  • 17. Genetic Algorithms
  • 17.1 Coding and operators
  • 17.2 Properties of genetic algorithms
  • 17.3 Neural networks and genetic algorithms
  • 17.4 Historical and bibliographical remarks
  • 18. Hardware for Neural Networks
  • 18.1 Taxonomy of neural hardware
  • 18.2 Analog neural networks
  • 18.3 Digital networks
  • 18.4 Innovative computer architectures
  • 18.5 Historical and bibliographical remarks.

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